vandana - Incedo Inc.

Wealth management industry is transforming rapidly as it pivots towards the fee based advisory model. The advisory model by nature requires a deeper level of relationship with the customers as compared to the commission based model which is more transactional in nature. At the same time, wealth managers are facing challenges from

Changing client mix and expectations,
Fee compression
Fintech disruption.

You can read details about how digital disruption is shaping the wealth management industry in our previous blog. Investment management has been commoditized and is no longer a differentiator. A robo advisor can perform portfolio allocation much better and at a much lower cost than a human advisor. If the advisors expect to charge more than robo advisory fee then they need to offer personalized financial advice based on holistic understanding of the client’s life stage, risk profile, investment objectives, preferences etc.

Issues with Traditional Segmentation Methods

The foundation of all personalization efforts is rooted in understanding the clients better and segmenting clients along the slices of client value, potential, demography, behavior etc. Client segmentation is informally practiced at wealth management firms but tends to suffer from some limitations

Static segmentation – Client segmentation is not a one time product purchase, it is a continuous and dynamic process. Customers move from one segment to another over a period and their investment preference, risk profiles can change based on their own life events or market conditions. For example, in the current zero interest environment there has been more demand for the riskier instruments even from the conservative investor segments. Traditional or one time segmentation tools are not able to consider drifts over a period and can therefore provide stale results.
Just focused on Value – Every practice or financial advisor at an informal level knows their most valuable clients as measured by assets under management or fees/ commissions earned. But value-based segmentation only provides you descriptive inputs about the advisory practice. It ignores other key parameters that can help in personalizing investment decisions, service models etc. Example: life stage has direct correlation with investment recommendation eg. 529 plans for mass affluents with young kids or IRA rollover recommendations for pre-retirees.
Not scalable – Informal or semi automated segmentation methods have trouble in scaling when the number of clients increase and segmentation variables multiply. Traditional segmentation models can place clients in one segment or another but tend to provide mixed results when the number of variables and data points increase. On an average an advisor has about 80-100 clients. If we are talking about a large advisory practice with multiple advisors or ensembles with a shared servicing model, it is not possible to keep track of all clients and their changing variables without automating it.
Limits personalization – The end objective of segmentation is not just to place clients in one bucket or another, it needs to inform the decision making process for personalized next best action. A static or non automated segmentation process stays mostly at macro level. To personalize client recommendations, micro segments need to be created and the manual segmentation methods struggle with that objective. Example: Within the retirees macro segment, the objectives, risk profiles and investment patterns of early retirees will be different from those of late retirees. In the accumulation stage, the investment objectives of investors with kids will be different from those with double household income and no kids. Unless we create micro segments, wealth managers will continue to provide advice which may be generic and at worst non contextual.

Segmentation needs to be dynamic, scalable, micro level and should inform the Next Best Action

Growing use of ML/ Data science in Client Segmentation

Although the use of data science and machine learning is growing in the wealth management space, the industry still lags various other consumer facing industries in using the full potential of Data and AI/ML. Today, there are multiple factors which are making it easier for wealth managers to use the power of machines to build segmentation engines.

The data sources and volumes have exploded and there is much more fine grained level of client data available than ever before.
There is large body of knowledge from the experience of other industries on how ML based segmentation enables data driven marketing
Lastly, cloud now allows for unlimited compute capacity by spinning concurrent workloads to perform complex processing and data analytics at minimal costs.

Various wirehouses, BDs, RIAs, technology providers over the last few years have started using AI to drive their segmentation model and recommendation engines. Machine learning based client segmentation can create data driven clusters which may not be readily visible via manual segmentation. Machine learning algorithms can analyze multiple deterministic features and analyze their correlation to create unsupervised clusters sharing homogeneous characteristics and behavior patterns. Such clustering does not suffer from any unconscious bias stemming from informal segmentation.

A scalable machine learning based segmentation model relies on the following data types and is able to slice customer data along multiple dimensions. Some examples below:

Segmentation Type	Based on	Segment Example	Data Required
Geographic & Demographic	Location, Age, Income, Profession, gender	Urban vs Rural Millennials vs Baby Boomers	Client & Account Data
Value/ Potential Value	GDC , AUM, Type of Revenue (Fee vs Commissions), Networth	UNHW, HNW, Mass Affluent, Masses High Value Vs Low Value	Trades Date, Advisory Billing data, Positions Data
Risk Profile	Goals, Risk Profile, Return objectives, Time Horizon	Conservative vs Aggressive Investor	Suitability Data
Behavioral	Trading Frequency,& Patterns	Passive vs Active Investor	Trades Data, Positions Data, CRM
Technographic	Engagement with Applications	Technologically challenged vs Tech Savvy Clients	Portal & App Analytics ( Number of logins, time spent)

This data can also be supplemented with external data to provide additional insights which may not be apparent from the first party data. For example, first party data such as client zip location when supplemented with external census data can provide valuable information about zip affluence, education level, demographic segment etc. Similarly, held away investments and accounts data can help paint a holistic financial picture of the client and determine the advisor’s wallet share.

Client Segmentation across Customer Journey

Let us see how client segmentation aids data driven decision making and helps in improving key metrics during the client’s journey:

Client Acquisition- RIAs can align their prospecting efforts with the client segment value proposition to ensure a larger prospect funnel and higher prospect to client conversion . As per the Schwab 2020 RIA benchmarking study, the firms that adopted an ideal client persona and client value proposition attracted 28% more new clients and 45% more new client assets in 2019 than other firms. Therefore the first step is to identify your target segment and align your messaging and marketing accordingly. example

A business development campaign aimed at Pre retirees and retirees needs to focus on themes of safety & capital preservation while one focussed on young professionals will focus on themes of growth and return. Segmentation engine can identify geographic areas which are likely to have prospects that match the firm’s target segments and where a particular campaign will find most resonance
In another example, the segmentation engine can classify the leads and prospects data into specific segments by matching the lead characteristics with existing client segments. Segmentation engines can predict if a lead is likely to become a high value customer and also suggest the kind of campaign that will appeal to them.
Wealth Managers are now combining client segmentation and advisor segmentation to predict and match which advisors will best serve a prospective client based on prospect’s preferences, life stage etc

Wealth managers are now using segmentation for matchmaking between clients and advisors

Client Growth- To capture the greatest wallet share of their clients, advisors should tie the investment recommendations to the client’s demographic, psychographic and risk segmentation. We talked earlier about recommendations for 529 plans for investors with young kids and rollover recommendations for pre retirees. Some more examples on how customer segmentation engines are feeding into next best action platforms to provide contextual recommendations for clients:

Growing popularity of ESG with the younger investors or the increased sales of life insurance to the urban middle age group during pandemic are good examples of how advisors and product companies align product recommendations with client segment’s preferences.
Similarly, if the clients are more focused on increasing their retirement savings, then recommendation around how they can contribute more than defined limits using backdoor roths will be appreciated by client
If the client is in a high tax bracket currently, then the advisor needs to recommend tax deductible IRAs while if they are going to be in higher tax brackets during their retirement, then Roth IRAs may be a better investment vehicle.
When segmenting based on the client’s browsing behavior, wealth managers can also send research reports and/or articles pertaining to sectors/ investment products that the client searches for in the portal. In addition, the portal can provide these inputs to advisors on the client dashboards for their next conversation.

As technology such as direct indexing mature further, clients will increasingly ask for customization based on their values, preference, beliefs and the wealth managers will have to offer customized portfolios at scale.

Client Servicing- Effective segmentation also helps in building a tiered service model with differentiated services to the most valuable clients and repeatable services to all other clients. Some wealth management firms craft personalized experiences for their top clients based on their hobbies and interests. Psychographic and technographic segmentation also help in devising the service channel for the clients. For example,

Clients that delegate all their investment responsibilities to their advisors and give them discretion on their accounts tend to prefer a light touch servicing model.
Clients who want high touch services and want to validate investment decisions would be more impressed by detailed research and analysis.
While a third category technically savvy clients want to have all the information , portfolio and plan details available anytime anywhere and prefer online service channels

Client Retention- A client mix skewed towards low value and unprofitable clients can encumber and advisory practice’s service levels & profitability and can put the most valuable clients at attrition risk. Many wealth managers have their bottom 50-60% of clients contributing only about 5% of their revenues and the top 20% accounting for more than 80% of the revenue. Therefore, advisors should periodically shrink to grow better. Client segmentation can help wealth managers prioritize clients for retention and for letting go based on current value, potential future value, influence potential. Ageing baby boomer population has brought on another kind of client attrition risk for advisors. As per industry studies, the financial advisors are not retained 70% to 90% of the time when the wealth transfers to the next generation. The client retention efforts in such cases therefore not only need to focus on the immediate clients but also on the next generation.

Segmentation is a growth as well as a Defensive Imperative

Thus, ML based segmentation can greatly aid data driven marketing efforts for wealth managers and lead to a higher return on the marketing dollars. It leads to measurable efficiencies in client servicing, attracting more clients and retaining high value clients. Lastly, it lays the foundation for a personalization engine for targeted recommendation, communication and servicing. While we have focussed above on how client segmentation can turbocharge an advisory practice’s growth, it is also a defensive imperative for the wealth management industry. FAANGs have perfected customer segmentation and personalization to an art form and are eagerly eyeing trillions of dollars of the wealth management industry. Wealth managers would do well to weaponize their data by using the power of machines and insulate themselves against the looming threat of big pocketed disruptors.

FAANGs have perfected customer segmentation and personalization to an art form and are eagerly eyeing trillions of dollars of the wealth management industry

To achieve the full promise of ML based segmentation, data infrastructure needs to support running of segmentation models at scale. To paint a holistic client picture, wealth management firms also need to break data silos and ensure availability of high quality, harmonized and consumable client data. Our next blog will discuss the data challenges in the wealth management industry and how the modern data management techniques can help overcome these challenges. Till then Happy Segmenting.

The current AI adoption levels coupled with improved machine learning techniques, is enabling companies to discover and operationalize business insights, previously hidden. Increased data availability and higher processing power is facilitating greater adoption, allowing enhanced methods with more data at lower cost.

Telcos world over are well positioned to make the most of this evolving situation by optimising their business and serving as AI platform enablers for other industries that are expected to spend upwards of $15bn annually on AI-led operations by 2021-22.

Three key trends that will define Telcos’ next decade are the reinvention of asset-light business models, resurgence of Telcos’ enterprise business, and liberation of network infrastructure that will accelerate and create a range of varied business models. Companies that embrace oncoming changes and those that make bold and quick investments will lead the pack and buck the trend.

Ovum’s findings reveal that the Telco market is perceived as second most at risk of disruption only after Healthcare. AI will play a large part in this change as Telcos look to implement technologies that reduce costs through greater automation, improved customer service, and optimized network and traffic management.

Significant improvements through AI will impact Customer Service, and Advertising/marketing operations, translating to improved cash flow for Telcos by 4x over the next 10 years.

Drivers of AI in Telco Ops

Artificial intelligence serves as an extension to a robust operational analytics system. However, whilst technology has been around for long, four powerful forces have accelerated greater adoption of AI.

Increased data availability: Available data on average is doubling every twelve months, attributed to increase in connected devices and much higher rate of data from connected devices. Evolved statistical models for decisioning are enabling new ways of managing and analysing very large data sets.
Data transformed towards a 360-degree view: Typically, Telco data is not integrated at a deep level, but instead scattered across functional and operational silos. The different data sets can vary widely in terms of quality and depth, rendering some data sets less actionable than the other. AI-powered solutions are helping integrate first & third-party data to maximize the ability of Telcos to achieve genuine 360-degree data view.
Increased processing power: Exponential growth of processing capacities is enabling AI solutions to be implemented on higher data volumes at lower cost. Additionally, distributed networks such as cluster computing on cloud have become exponentially more powerful.
Improved machine learning capabilities: Commercial interest in AI/ML has been growing strong with technology devoting significant capital to research. As a measure, Google doubled its AI research in the four years 2012-16.

Impact of AI on Telcos

There are opportunities aplenty for Telcos to leverage AI, as summarized below. (Not exhaustive).

Customer Service & Contact Center Support

The advent of 5G in the telecommunications sector is driving multiple opportunities for organizations. 5G along with accelerated cloud adoption promises to deliver new digital services at a much lower latency through SaaS platforms, OTT services, and cloud based unified communication services, among others.

AI is a key enabler in improving quality of customer experience and quality of service. Telcos’ strategies to monetize data depend in large part on algorithmic intelligence and automation to handle exponential rise in traffic and onboarding new devices and users. Add to that, processing personalized customer service responses.

Key Opportunities

Assisted context-aware customer service: For customer service, the major reason why customers call is billing dispute. Using AI, Telcos can analyse user activity across engagement channels, and predict when a user calls in. Identifying intent and tailor their experience to get prompt resolution. Ex., when customer uses app or web to search billing pages, customer search data can flow into Telco Data Lake and finally when customer calls in, customer data from the Data Lake can be used to create context, which is plugged-in during agent call or chat.
Predictive maintenance of customer premise equipment: Analysis and insights of contact center notes to better the IVR system. With prior permission, analyse customer behaviour in terms of equipment used to signal a potential problem beforehand and pre-empt corrective action.
Conversational AI: Scaling and automating one-to-one conversations to drive down the cost and improve the efficiency of operations and customer service. Contact center first line virtual agent dealing with 90% of routine questions, with emotion sensing capability based on voice and video. Major use cases for implementation in the space of conversational AI are the regular ones that overwhelm contact centers. Ex.;. installation, set-up, troubleshooting and regular maintenance.

Network Optimization

AI is rooted in Telco virtualization of networks, namely, SDN and NFV. A fully NFV enabled network will be controlled by a single NFV orchestrator that dynamically determines critical network operations such as assignment of resources to a network function, provisioning new network nodes, or withdrawing network elements that go underutilized. Traffic will be controlled by a centralized SDN controller augmented by AI that allows for efficient and proactive routing of traffic enabling capacity to be managed effectively, network outages minimized, and faults bypassed. AI can optimize configuration of a Telco network according to dynamic network capacity demands, characteristics of traffic volumes, user behaviour, and other parameters. Network deployments may also be further improved by AI to predict traffic patterns and forecast user trends.

Key Opportunities

Telecom network capacity optimization and analytics: Transition to Network Function Virtualization (NFV), Software Defined Network (SDN), and Self-optimizing Network (SON). Self-optimizing network based on traffic information, detect anomalies beforehand and proactively optimize network performance.
AI for predictive network congestion and maintenance: Utilizing data, sophisticated algorithms and machine learning techniques based on past data. This helps in close monitoring of equipment, proactively anticipate failures and take corrective action before the customer raises the ticket.
Pre-emptive Vulnerability and fraud detection, cyber security helps defend critical network infrastructure from malicious attack.

Marketing Engagement

Understanding user behaviour enables Telcos to create personalized customer engagements for its customers, creating offers and messages that are contextual and performed in real-time across a wide range of criteria, including personalized pricing plans, service bundles, and marketing messages. Personalized, real-time sales and marketing offers play a central role in Telcos’ data monetization strategies as well as enhance value of customers’ engagements and improve Customer Satisfaction (CSAT) and Net Promoter Score (NPS).

Key Opportunities

AI for faster response to personalized sales and marketing triggers, such as creating and tearing down offers. Product bundling recommendations.
AI-led customer engagements to replace manual intervention in select sales & marketing business processes.
New entries in product catalogues optimized by AI, such as price and size. Deep learning of competition and advertising data can be used to configure these entries.

AI Challenges for Telcos

Telcos face numerous challenges as they consider their next steps with AI, and they would need to keep a watch on the following factors, and they play out.

Threat from early movers: Telcos are not the only players looking to leverage AI to improve operations and services to gain competitive edge. Consumer tech. OTT and FANGs of the world are investing heavily in AI, and Telcos fear being left behind.
Data privacy: AI can leverage very granular consumer data insights and these capabilities will deepen going forward, to the point where they attract regulatory scrutiny. Telcos should ensure their AI solutions can safeguard data privacy.
Realign workforce for new employment opportunities and talent retention: While the job market will evolve because of efficiencies and productivity introduced by AI-led automation, this will open up new possibilities for the workforce. Our workforce will steer AI initiatives, set goals, provide data and training, and monitor machine activities and performance. Fragmented AI skills in the organization, unclear AI organizational model results in difficulty while attracting and retaining AI talent.
Lack of end-to-end operational visibility: Numerous isolated POCs being undertaken by Telcos in the AI space make it difficult to present the comprehensive value and hence lead to insufficient leadership support for projects.
Lack of effective change management: Understanding of AI at different levels, unclear impact and value extraction and sporadic AI related training and adoption efforts makes it difficult in percolating changes to all levels uniformly.

How does all this shape the future of Telcos

In a decade from now, a new breed of asset-light carriers with more sustainable businesses will emerge as technology advances and evolving consumer demands reduce costs across operations.

There is more promise AI and digital technologies holds for Telcos, now more than ever before, which now enables asset-light carriers to operate customer-facing functions at drastically lower costs by substituting computer processing power for people. Both consumer and business customers are increasingly comfortable with digitally delivered self-service options, providing an advantage to carriers that build their businesses this way.

What is within the realm of possibility is that carriers could successfully operate with no stores, no call centers and significantly fewer field technicians.

We are seeing early experimentation with this among Telcos globally, where there are digital only wireless services from large incumbents, namely Verizon’s Visible plan in the US, and fixed wireless broadband Starry’s resource-light model in the US. Over the next decade, this shift toward digitalization and automation could set off a chain of similar events in the Telco market.

Proliferation of asset light carriers will deploy this strategy first, and their lower breakeven point will allow them to focus on targeted, smaller slices of the market. These carriers will be either standalone businesses or new arms of existing companies in other sectors that have strong branding and distribution that they can use to push a telecom offering.

Enhanced profitability of these new market entrants will put pressure on traditional Telcos’ price points and revenue, forcing them to respond with their own digital front ends. As per Ovum research, average Net Promoter Score for early adopters has risen by over 20% points, driving up revenue by up to 10%, while lowering customer-facing costs by over 30%.

Emerging technologies deployed in contact center operations cut down incoming customer calls and improve operations cost. As per BCG analysis, deflection rate of 20%-40% for customer calls can result in lowering the contact center cost by 10%-20%.

Taking a deeper look into this potential, AI implementation results in dual benefit for Telcos. On one hand it generates a potential of ~10% increase in revenue, while on the other hand also helps in cutting down the cost by ~15% across the value chain. Few areas gaining traction on the revenue side are lead generation and personalization, managing customer churn rate, upselling and cross selling offerings to customers. Similarly, on the cost side, network optimization, and workforce optimization.

Incedo’s solutions tailored to meet demands of the AI-led Telco world

Incedo has built a set of AI/ML pipelines that can be configured to solve a variety of operation optimization use cases. These pipelines can automate processes, tap into unstructured data sources for intelligence. Our solutions are driven by the following:

Cross-industry techniques: Inspired by use cases across industries – eCommerce Recommendation Engines, Image Search by Google.
Automated model training: Designed with AutoML features for automated learning across ensemble of techniques.
Self-learning in production: Algorithm to identify model performance and run calibration in production. Like Google Maps suggest better routes when found.
Modular design: Developed as Lego blocks for easy integration into existing infrastructures.

Incedo’s AI/ML Pipeline Tailored for Telco applications

AI/ML Pipeline	Objective	Techniques	Telco Applications
NLP Pipeline	To make sense of free form text data	Topic Modeling – LSA, LDA, RNN Classification – LDA2Vec, SGD, Random Forest Sentiment – Stanford Symantec Libraries	Analysing Jeopardy Tickets Automated test case generation
Optimization Pipeline	To prioritize between actions based on predicted impact	Markov Decision Processes – Markov Chains, Hidden Markov Models, Multi-Armed-Bandit, Reinforcement Learning	Emerging Hot-Spots End-Point Placements Personalizing Customer Interactions
Anomaly Detection Pipeline	To identify or tag anomalies from a normal behavior	AR Family of Models, Auto Encoders, LSTM, Isolation Trees, Neural Nets	Predictive Maintenance
Personalization Pipeline	To personalize customer touch points – email, store, website, app, customer service	Propensity Models to estimate intent Reinforcement Learning to optimize content delivery in the real-time.	Web Personalization Cross-Sell/Up-Sell Customer Retention

Incedo is using its AI/ML pipelines to help a US based Tier 1 Telco to enhance its spectrum of network operations and customer service.

Predicting optical network faults 48hrs. in advance to reduce trouble calls or expensive technician dispatches
Automatic redirection of 5G build out issues to the right team for quick resolution.
Automated ticket logging by extracting relevant information from emails, with the help of NLP based email tagging solution.
Prioritize automation opportunities by identifying process gaps and bottlenecks using data mining algorithms.
For customer service, a major reason why customers call is billing dispute. Using AI, Telcos can analyse user activity across engagement channels, and predict when a user calls in. Identifying intent and tailor customer experience to provide prompt resolution.

Covid-19 and the aftermath: Impact on the industry

We are in the middle of one of the worst health crises the world has experienced in decades and COVID-19 has not only caused socio-economic disruption but also impacted nearly all sectors and geographies across the globe. Wealth management is one of the vulnerable sectors with highly correlated revenues to capital market performance. Despite recovery in capital markets in recent weeks especially in the US, many WMs have not seen their assets to pre-Covid levels as many European and Emerging markets are still much lower than pre-Covid levels .This has accentuated the pressure on revenues and calls for cost optimization & prudence on middle-back office functions.

Wealth management operations perform some of the most critical tasks including client onboarding checks, account setup, trading, asset transfers, etc. The immediate impact on operations was managing extremely high trade volumes and ensuring that critical processes continued to run smoothly. Most firms did not have business continuity and operations readiness plans for an event of this nature. Firms must therefore realize that this adversity presents an opportunity to resolve immediate priorities (BCP, automate critical and high effort tasks, etc.) and redefine longer term strategy to align with the paradigm shifts for an optimized operations framework.

Even before Covid-19, there was a paradigm shift that was already underway in Wealth Management operations and the pandemic merely exposed or amplified the need for Next generation operations transformation. Primary drivers of the shift were client expectations for personalized portfolios and changing priorities, growing regulations and need for real-time compliance reporting and increased competition from FinTech.

As an example, trade operations teams have always had pain points including manual reconciliation leading to delays in trading, lack of straight through processing, lower accuracy and increasing processing time, etc. Firms with higher operations maturity have relied on automation investments for e.g. automated settlement and reconciliation to minimize the impact of volatility due to COVID. Firms with lower maturity have had to rely on shuffling teams to manage trades, resources spending longer hours to complete daily trading and settlement.

What will it take to win in a post Covid world?

In today’s world, operations must not be seen as just ‘support’ but a mission-critical function. This is because acquisition costs can generate a compelling ROI only over the next 3-5 years when the wallet share is deepened. For deepening of wallet share, delivering a superior CX is critical which can happen only when wealth management firms can exceed advisor and client expectations.

For immediate resolutions, the firms should perform Next generation operations transformation’ strategy can help wealth management firms with automation capabilities, process mining and outsourcing to drive maturity and bring efficiencies

The framework should be built out in modular fashion for reusability.

Incedo believes for firms to emerge as winners in the long term, they must consider three key shifts in the way operations are managed and run. Next generation operations transformation’ strategy can help wealth management firms with automation capabilities, process mining and outsourcing to drive maturity and bring efficiencies.

Change the objective from cost efficiency to customer experience. An optimal and holistic client experience involves minimal manual touchpoints, fewer documentation and faster turnaround time for onboarding, account maintenance requests, asset transfers etc.
Wealth management firms should aim to “Re-imagine processes” rather than focus on ‘process standardization’. It goes beyond process standardization by mining data, deriving insights and determining best action to digitize and automate sub-standard processes
Firms need to focus on outcome driven KPIs rather than traditional transaction SLAs. Derive success metrics of the end to end process rather than measuring siloed metrics. For e.g. for client onboarding the key outcome to focus on is when the account is funded and ready for trading rather than measuring individual process steps for submission, set up, etc.

Over the last few years, firms have invested in automation and process improvement initiatives but have not been able to achieve maturity in their operations transformation journey.

We believe that these initiatives are not realizing their expected outcomes because:

Automation solutions deployed in silos instead of reviewing the overall customer journey
Firms are automating the current underlying processes as-is which could be inefficient and hence not achieving higher returns on investment
Focus on automating the product features rather than customer journey
Data & AI not collected and analyzed sufficiently to perform data driven decision making

To learn more about how to rethink your next generation operations transformation initiatives and how Incedo can partner with you in your journey, mail us at inquiries@incedoinc.com

Observations from the market

The Covid-19 global pandemic has positioned Telcos at the forefront, in a mission critical mode as an essential services enabler, ensuring continuity in business operations during the pandemic. Advanced technology such as AI powered Digital Experiences, Automation & 5G connectivity are proving vital in delivering solutions to help fight the pandemic. While the adversity has opened up interesting opportunities, there are challenges aplenty.

In this rapidly evolving business environment, the new normal includes enforcement of physical distance and work from home that has created challenges in executing daily activities, work, supply chain and logistical delays, causing delayed initiatives and missed opportunities.

The Covid-19 pandemic has had a substantial impact on Telecoms and allied industries, though it fares slightly better than other sectors like manufacturing, and hospitality, and that no sector is immune to the pandemic, but some will suffer more than others.

Immediate Factors affecting Telco business

There are several reasons that will cause Telcos to revisit their 5G capex programs and deal with delayed timelines due to

Global supply chain disruptions,
Availability of 5G network devices,
Delays in formal standards definition,
Spectrum auction delays,
Delays in 5G infrastructure permits and inspections,
Closure of retail stores,
Availability of 5G supported mobile devices,

Immediate Business Impact on Telcos

Revenue Impact
- Communication service revenues declined 3.4% YoY in mature markets.
- International roaming revenues declined ~6% of billed revenue/year, especially in tourism heavy countries.
- Freebies and waivers offered to retain retail subscribers.
- Decline in global SME ICT spend on enterprise revenues.
Existing network optimization to deal with surge in network traffic Telcos are spending on improving existing capacities addressing overall network resilience which was positive over the past four months with an increased focus on traffic management, absorbing 10% – 70% spike in network traffic reported across Telcos.
Capex & Infrastructure Rollouts Supply side disruption slowed down 5G and fiber rollouts, with a reduction in capex. Potential upside in 2021 broadband demand anticipated to fast-track roll outs.
Supply chain Global smartphone shipment to decline 3.1% YoY in 2020. Production slow-down in Asian manufacturing hubs impacts global supply of panels, touch sensors and printed circuit boards.Trade wars notwithstanding, and aversion to Chinese 5G equipment vendors from deploying equipment indicated by several countries globally, including India; alternate suppliers better be prepared to step in to fill in the resultant demand-supply mismatch.

Economic recession slowing down device and service upgrades and suppressed 5G demand will defer Telco plans of aggressive deployment strategies.

As per Statista, the device segment, including PCs and phones could see the steepest fall, currently projected to decline 12.4% in 2020 compared with the previous year. Infrastructure will be the least affected segment according to the adjusted forecast with projected growth of 3.8% in 2020, as businesses keep utilizing cloud deployments. Thanks to the cloud and greater inclination towards “software defined”, technology infrastructure is the only segment in global IT to grow in 2020, while other segments are projected to decline.

Imperatives for Telcos to emerge as winner in a Covid-19 hit world

In light of the new normal, leading Telcos are addressing three dimensions of managing a crisis; respond, recover, and thrive, which predominantly includes digital technologies.

Experiences that are built on a foundation of customer-first

Calibrated strategy moving from digital-first to digital-throughout

Drive AI and automation programs broadly organization-wide and at far greater speed – network, customer, IT, front desk, and back office.
Enhance digital customer experience through self-service channels and support journeys. Intelligent BOTs to serve more than a bridge to the call center for complex queries and increase in AI-agent customer interactions.
Build and evolve capabilities to support new Telco value propositions, such as Software Defined Networks, Cloud networking, and Intelligence at the Edge.
Continuous testing of network reliability through intelligent automation.

Rapid scalability and resilience of network operations in a hyper-connected 5G internet of everything world Network operations

Overall network resilience was positive over the past four months with increased Telco focus on traffic management, absorbing 10% – 70% spike in network traffic reported across Telcos.
As an instance, leading US Telco Verizon is projected to spend $18.5 billion this year on improving its network resilience, a $500 million increase over its planned spend triggered by demand surge during the pandemic.

Increased spend in building network resilience will be channeled towards modernization of legacy systems that need to scale up to demand.

Telcos and broadband providers are working overtime to cope with demand spikes and maintain reliable connectivity through capex spends, augmenting and optimizing their current wireless and broadband networks. However, the inability to monetize this investment in the short term, and deal with challenges from declining sales and roaming revenue, retail chain closures, relaxing limits and out of bundle charges are hurting the earnings.

Reduced cycle time in innovation and change deployment

The power of AI-based technology combined with 5G when blended with distributed edge computing, cloud and IT functionalities will drive the next wave of innovation. The pace of change will compel innovation cycle times to reduce drastically to make solutions relevant to the current times.

Promise of 5G technology & innovation in a pandemic

5G tech. can address prevailing connectivity and network performance challenges, more so during a pandemic, as it enables the transformation of public health and offers possibilities of new treatment methods. Leveraging advantages of speed, latency, number of connection points and range, apply to the following 5G enabled use cases.

Real-time thermal imaging of people in motion in public spaces,
Smart robot in Tele-medicine, remote diagnosis, consulting, and emergency treatment
AR/VR in Tele-education and Tele-conferencing
Smart transportation, unmanned vehicle solution

Cost optimization & efficiencies to gain prominence by moving from fixed cost to variable cost model

Transition likely from fixed cost to variable cost model to reduce operating leverage, translating to following shifts:
- Pay-per-use for O&M, and AMC,
- Network utilization and consumer demand based site rentals,
- Retail store redundancies,
  - Automation of business processes for corporate efficiencies such as finance, tax, billing and operations,
  - Consolidation, mergers, and fundamental shift in market structures with formation of Netcos.

Incedo’s service offerings to engage with Telcos

Post pandemic, the economic recovery will happen at a very fast pace that will spur consumer and business confidence starting Q3-Q4. It’s a fact of life that there will be an immediate set back on the supply chain, investments, operation and delivery due to Covid-19. It is unlikely to have a lasting effect in the direction emerging for Telecoms since the middle of the last decade, which is characterized largely by high speed, low latency 5G experiences, digital transformation, AI, virtualization, automation, and accelerated transition from traditional voice to unified communications.

Overall Consulting and Technology Services like ours will need a re-calibrated strategy of delivering solutions focused on immediate needs through digital & automation that will help tide over the current situation.

We help clients unlock the full potential of 5G as it promises to transform the industry. We leverage the client’s digital and data infrastructure to deliver world class customer experience and in optimizing their networks & biz operations.

Incedo solutions for Telco to help tide over the pandemic crisis

Legacy Systems Modernization
- Platform re-engineering
- Application modernization
- Cloud engineering & migration services

Digital Transformation and Analytics

Combining design thinking, data-informed decisions, iterative experimentation backed by our integrated data science, UX and innovative engineering capabilities provide clients a full-stack solution.

Next wave of innovation & engineering will be driven by the power of AI combined with 5G, blended with distributed edge computing, cloud and IT functionalities. Incedo is at the intersection of the blend to deliver innovative engineering solutions.
Security takes on an even larger stage with high reliance on economic activity on Telco networks. Incedo’s cyber security solutions address growing vulnerabilities in networks going by the numbers of phishing and malware attacks targeted at remote working.

Cloud Cost Optimization:

Cloud has a decentralized model of consumption where each department or BU has visibility into their cloud consumption thanks to fine grained Account creation and control and billing segregation. The decentralized model has raised costs for organizations exponentially, and often without any control over the spiralling bottom line. Businesses will have to start to get a handle on these costs as cloud usage grows, streamlining the expenditure that they are not utilizing to full effect, and cutting out duplicate spending or unnecessary overheads.

This also provides an opportunity to vendors who can build their tools and services around cloud optimization services.

Hybrid Cloud environments take a big jump with Focus on Automation:

With cloud-native computing [and] container-based workloads gathering steam, enterprises will want to build solutions that take advantage of their on-premise resources and cloud resources equally adding that in some organizations cloud utilization will be driven by specialized circumstances or use cases rather than as the default setting. Serverless architecture driven by containerization and orchestration engines will hybrid cloud approach easier.

Managing complexities arising from multi cloud environment is possible by automation tools along with comprehensive dashboards that provide a holistic view into cloud operations

Delayed migrations due to Insufficient IaaS skills:

According to Gartner “Through 2022, insufficient cloud IaaS skills will delay half of enterprise IT organizations’ migration to the cloud by two years or more. Today’s cloud migration strategies tend more toward “lift-and-shift” than toward modernization or refactoring. However, lift-and-shift projects do not develop native-cloud skills. This is creating a market where service providers cannot train and certify people quickly enough to satisfy the need for skilled cloud professionals”.

To overcome the challenges of this workforce shortage, enterprises looking to migrate workloads to the cloud should work with managed service providers and SIs that have a proven track record of successful migrations within the target industry. These partners must also be willing to quantify and commit to expected costs and potential savings.

Security, reliability, and flexibility drive cloud strategies :

The early convention of cost savings by moving to cloud is no longer the only one although an important one. However security , reliability and flexibility have become key driving factors in mutli-geographic and multi-vendor environment.

COVID-19 pandemic has brought a significant change in the way financial advisors manage their practices, clients and home office communication. Along with the data driven client servicing platforms, smooth transition and good compensation, advisors are closely evaluating their firm’s digital quotient to provide them the service and support in times of such crisis and if not satisfied, may look out options of switching affiliation during or post this crisis.

This pandemic is not a trigger rather has provided additional reasons for advisors to continue to look out for a firm that fits better in their pursuit of growth and better client service.

2018 Fidelity Advisor Movement Study says, 56% of advisors have either switched or considered switching from their existing firms over the last 5 years. financial-planning.com publishes that one fifth of the advisors are at the age of 65 or above and in total around 40% of the advisor may retire over the next decade.

A Cerulli report anticipates transition of almost $70 trillion from baby boomers to Gen X, Gen Y and charity, over the next 25 years. Soon, the reduction in the advisor workforce will create a big advice gap, that the wealth management firms will have to bridge by acquiring and retaining the right set of Advisors

We are observing a changing landscape of advisor and client population, mounting cost pressure due to zero commission fee and the need for scalable operations. COVID-19 has further accentuated the need for the firms to better understand the causal factors for changes in advisor affiliation, to optimize their resources deployed for engaging through the Advisor life cycle. The wealth management firms are increasingly realising that a one fit for all solution may not get optimal returns for them.

Data and Analytics can help the firms segment their advisors better and drive better results throughout the advisor life cycle. Advisor Personalization, using specific data attributes can deliver contextual and targeted engagements and can significantly improve results by dynamically curating contextual & personalized experiences through the advisor life cycle.

A good data driven advisor engagement framework defines and measures key KPIs for each stage of the advisor lifecycle and not only provides insights on key business metrics but also addresses the So What question about those insights. As wealth management firms collect and aggregate data from multiple sources, they are also increasingly using AI/ML based models to further refine advisor servicing.

Let us look at the key goals or business metrics for each stage of the advisor life cycle below and see how data and analytics driven approach helps in each stage of the life cycle.

Prospecting & Acquisition

To attract and convert more high producing advisors, recruitment teams should be tracking key parameters through the prospecting journey of the advisors so that they can identify:

What is the source of most of their prospective advisors; RIA, wirehouses, other BDs
Which competitors are consistently attracting high producing advisor
What % of advisors drop from one funnel stage to another and finally affiliate with the firm
What are the common patterns and characteristics in the recruited advisors

Data driven advisor recruitment process that relies on the feedback loop helps in the early identification of potential converts, thereby balancing the effort spent on recruited vs lost advisors. It also improves the amount and quality of the recruited assets.

For example, analysis of one-year recruitment data of a large wealth management firm revealed that prospects dealing with variable insurance did not eventually join the firm due to the firm’s restricted approved product list. Another insight revealed that prospects with a higher proportion of fee revenue vs the brokerage revenue increased their GDC and AUM at a much faster rate after one year of affiliation. Our Machine Learning Lead Scoring Model used multiple such parameters and scored a recruit’s joining probability and 1-year relationship value to help the firm in precision targeting of high value advisors. These insights allowed the firm to narrow down their target segment of advisors and improved conversion of high value advisors.

Growth & Expansion

A lot of focus during the growth phase of the advisor lifecycle is on tracking business metrics such as TTM GDC, AUM growth, commissions vs Fee splits. The above metrics however have now become table stakes and the advisors expect their firms to provide more meaningful insights and recommendations to improve their practices. Some of the ways, firms are using data to enhance advisor practice are by:

Using data from data aggregators and providing insights on advisor’s wallet share and potential investment opportunities
Providing peer performance comparisons to the advisors
Providing next best action recommendations based on the advisor and client activities

For example, our Recommendation Engine analysed advisor portfolio and trading patterns and determined that most of the high performing advisors showed similar patterns in Investment distribution, asset concentration, churning %. This enabled the engine to provide targeted investment recommendations for the other advisors based on their current investment basket and client risk profile. The wealth management firms are also using advisor segmentation and personalization models based on their clients, Investment patterns, performance, digital engagement, content preference and sending personalized marketing and research content for the advisors based on their personas thus driving better engagement.

Maturity and Retention

It is always more difficult and costly to acquire new advisors as compared to growing with the existing advisor base. The firms pay extra attention to ensure that their top producer’s needs are always met. Yet despite their best efforts, large offices leave their current firms for greener pastures or higher pay-outs. The firms run periodic NPS surveys with their advisor population which indicates overall satisfaction levels of the advisors, but they do not generate any insights for proactive attrition prevention. Data and analytics can help you identify patterns to predict advisor disengagement and do targeted proactive interventions.

For example, our attrition analysis study for a leading wealth manager indicated that a large portion of advisors over the age of 60 were leaving the firm and selling out their business. This enabled the firm to proactively target succession planning programs at this age demographic of advisors. Our analysis also indicated a clear pattern of decreased engagement with the firm’s digital properties and decreasing mail open rate, for the advisors leaving the firm. Based on factors such as age, length of association with the firm, digital engagement trends, outlier detection, our ML based Attrition Propensity model created attrition risk scores for advisors and enabled retention teams to proactively engage more with at-risk advisors and improve retention.

As per a study from JD Power, wealth management firms have been making huge investments in new advisor workstation technologies designed to aggregate market data, client information, account servicing tools and AI-powered analytics into a single interface. While the firms are investing heavily in technology, only 48% advisors find the technology their firm is currently using, to be valuable. While only 9% of advisors are using AI tools, the advisor satisfaction is 95 points higher on a 1000-point scale when they use AI tools. Advisors find a disconnect between the technology and the value derived from the technology.

This further necessitates the need for personalised solutions for advisors and an AI driven Advisor personalisation platform which provides curated insights to the firms. This helps in targeted & personalized services & support to advisors through the Advisor lifecycle, enabling optimal utilization of the firm’s resources and unlocking huge growth potential.

The firms that will understand the potential of data driven decision making for their advisor engagement and will start early adoption of such tools will thrive in these uncertain times and will emerge as a winner once the dust settles.

Digital Transformation was one of the most important business trends across the wealth management circles before the unprecedented global disruption shifted all the focus towards ensuring business continuity. Recognizing the changing digital behavior, leading RIA custodians, broker dealers, TAMPS, RIAs had either embarked or were kickstarting their digital transformation journeys. The disruption caused by COVID 19 has clearly laid bare the nascent stages of digital evolution for the many wealth management players. The customer service centers are overwhelmed with increased call volumes and reduced capacities. Similarly, financial advisors are required to field multiple long calls from anxious clients who are uncertain about their investments. Low adoption of digital assets provided by broker dealers and RIAs firms may be a result of sub optimal CX or gaps in information availability. We all may be in a long period of disruption, and the firms that are not able to drive digital adoption, or continuing to remain person dependent will realize the difficulty in client servicing, let alone operational scaling. Digitalization needs to be looked at as an essential part of the wealth manager’s business continuity efforts as it ensures information availability and provides online self-service capabilities. Digital Insulation is another complementary term that offers an ambitious glimpse of future possibilities. To protect their businesses from personnel-related disruptions, organizations will need to invest digitalization and thus ensure business continuity.

Drivers of Digital Transformation

Digitalization in the wealth management was primarily driven by the following drivers:

Changing business model– The business model has been steadily shifting away from a product focused brokerage model to a relationship focused advisory model. In a study conducted by https://www.financial-planning.com/ , the consolidated commissions revenues for the top 50 Independent broker dealers have reduced in the last 5 years, while the advisory fee has increased by more than 50% in the same period. Shifting client base of advisory clients expect engagement across multiple channels and customer experience becomes paramount.
Revenue compression– Zero commissions are already a reality and were a seminal event for the industry. Revenue impact for the players will range from anywhere between 10%- 20%. Also, RIA custodians are likely to levy additional fees on the participants to cover for the lost revenues. With the fed rates likely to remain low for the foreseeable future, the revenue stream from sweep accounts will also reduce substantially further, thus accentuating revenue pressures.
Changing the age mix of client and advisors– As the wealth transfers from baby boomers to the millennials, the millennials will make up for an increasingly valuable client segment. Similarly, as the ageing advisor population retires, the new advisors will primarily be dependant on technology, largely influencing their business decisions.
Fin Tech Disruption– Advisor Fintech tools, also known as Advisor tech, have not only invaded the usual favorites domains such as CRM, financial planning, and portfolio management but have created new advisor tech segments such as mind mapping, account aggregation, forms management, social media archiving etc. 2020 T3 advisor software survey covered almost 500 different tools across almost 30 sub segments highlighting the plethora of tools available for clients and advisors.

The above drivers are creating two main needs for the wealth management players:

Need to Scale Servicing. The first two drivers (changing business model and revenue compression) are forcing wealth management players to realize the need to digitalize and gain operational scale for servicing more clients. In a study conducted by https://www.refinitiv.com/en, servicing clients was cited as the most important digital driver for the wealth management firms. The ongoing disruption will further fuel the demand for straight through client onboarding, E- account opening, digital signatures, and workflow-based proposal generation solutions. Moreover, the organizations that still depend on back office processors to open accounts and onboard clients will see an increased transition. Similarly, advisors and clients need to be provided with tools to move to a more self-service model.

Need to Scale Knowledge– The last two drivers (changing Age mix & FinTech Disruption) trends have resulted in increasing client and advisor expectations. An increasing number of clients no longer just delegate their investment decisions to advisors but also seek to collaborate and validate the investment decisions. They look for real time knowledge about their current investment and investment insights. With the prevailing uncertainty, many clients will also start demanding real time information about risk tolerance of their portfolios are and how they can quickly pivot to either protect their investments or to take advantage of any profitable bargains. The clients will naturally drift towards financial advisors who provide full-service client portals to access and monitor their investment. Similarly, advisors will drift towards firms which provide digital practice management tools and advisor self service capabilities.

The third form of scale which will become very relevant in the current disruption is Scaling digital collaboration. With Social distancing becoming the norm, in person client meetings may not be possible for some time. While advisors and clients can still talk and make video calls, current tools do not allow for collaborative discussion or presentations. Going forward, the organizations will need to invest in tools that enable online client engagement and advice delivery as a complimentary engagement channel. Software providers can study the evolution of telemedicine systems, which provide a full suite of features including video conferencing, document sharing, scheduling appointments, taking notes, as well as client history. Once client portals or CRM systems can be enhanced for Tele Advice, this alternate engagement channel is likely to grow in popularity with both clients and advisors, allowing remote collaboration and engagement.

To sum up, digitalization is the best antidote for any such future disruptions, which will be assisting wealth management firms in modernizing advice and accelerate their digitalization efforts to not only transform but to insulate their businesses. Digitalization can in fact become a vital cog of the business continuity efforts by enabling self-service, information disintermediation and collaboration.

COVID-19 pandemic brought with it a complete disruption to the existing normal operating procedures in most of the industries. The unprecedented situation due to the pandemic has struck some of the business functions disproportionately hard. The most impacted functions in the companies however are those where the workforces relied heavily on “on the field” presence for the execution of their work compared to those functions which could easily be converted into a remote working setup.

From the Life Sciences industry standpoint, the drug promotion via Medical Reps (MR) falls into the prior category. Although the industry as a whole has seen rapid adoption of digital solutions across the workstreams in the ongoing decade, their marketing efforts to the Health Care Providers (HCPs) still heavily rely on the Face to Face (F2F) interaction of the Reps with the Physicians.

This status quo however has been challenged by the ongoing COVID pandemic, with the social-distancing norms in place. There are estimates of 92% drop in F2F HCP engagements in April 2020 compared to 6 months ago[1]. It is also estimated that in the new post-pandemic normal, the frequency of F2F engagements will shift as much as by 65% to quarterly/annual rather than the weekly/monthly norms prevalent pre-COVID². This is indeed a massive blow to the existing Pharma sales and marketing approach and has seen many of the companies rapidly scale up their digital engagement channels to fill the gap. The use of these digital channels for HCP engagement has seen a 2x increase from their pre-pandemic levels.[2]

The current COVID driven environment has several key implications for the Lifesciences organizations in their effort to meaningfully engage with HCPs.

Impact on Sales & Marketing Channel Mix – Restrictions on in-person meetings have lead to reduced access to HCPs, canceled/postponed training sessions, and canceled conferences and events, all of which were major marketing methods till now. Pharma and other Lifesciences companies have to accelerate their sluggish digital transformation initiatives and enable a true omnichannel digital experience for HCPs
Digital engagement channel optimization – The digital omnichannel push needs to account for varying pysician preferences for the type of digital channel engagement, based on factors like therapeutic area, demography, and personal preferences.
Personalized, contextual messages for better engagement– Physicians at the front lines have to balance innovation and efficiency while dealing with the increased pandemic workload. As a result, engagement and interaction frequency with HCPs have decreased abruptly. With this sudden shift, there is a need for communication to be crisp and contextual for it to be effective.

This brings us to an important question of how the Bio-Pharmaceutical companies should navigate the current shock concerning HCP engagement and what lies ahead for them. Pharma Commercial Teams would need a strategic HCP engagement approach that manages the immediate COVID situation as well as builds capabilities for the new digital-driven normal.

As the Bio-pharma companies scramble to optimize their marketing efforts in the current times, they need to formulate a strategy which tackles the problem in phases:

Now: Immediate Priorities to manage COVID situation (next 1-3 months) – Set of tactical initiatives and workarounds to the existing HCP engagement methodologies, meant to strictly tackle only the immediate priorities around COVID-19 impact
Next: Accelerate digital capabilities build-up to drive Omnichannel HCP engagement (in 3-6 months ) – Strategic initiatives to accelerate and deliver a highly engaging digital experience for HCPs. These will fundamentally help in shifting and realignment of biopharma omnichannel engagement capabilities in post-COVID realities.

(Now) Immediate Priorities to manage HCP promotions in COVID situation

As an immediate measure, Bio-Pharma companies need to evaluate the impact of COVID-19 on HCPs’ practice – Rx, patient counts, geographical impact, etc, and Field Reps access to HCPs. It is imperative that Biopharma companies create a COVID control room, which integrates external trigger impact data with internal data sources to truly assess the impact of COVID situation (and potentially other external triggers and shocks) on their sales & marketing plans.

As the COVID impact is quantified, bio-pharma can synthesize the same to adjust the tactical call plans for their promotional activities. The critical parameters to consider while making changes to the call action models would be:

Incorporate external COVID impact triggers at geo, HCP level
Defining and quantifying the digital affinity of physicians
Optimization of cross-channel (Digital & Rep) targeting frequency
Dynamic adjustments to the call-plan (digital mix, frequency) as the COVID situation evolves

(Next) Accelerate digital capabilities build-up to drive Omnichannel HCP engagement

Once the immediate priorities related to the pandemic are solved, companies can utilize the learnings and key insights from the pandemic times to further advance their digital engagement strategy. The evaluation of what went right and what were the misses in the earlier stage should also be used to formulate a long-term digital and omnichannel engagement strategy. There is also, a lot to learn from Digital-natives who have, highly effectively, leveraged digital channels to driven customer engagement.
Bringing these best-practices from Digital natives together with Bio-pharma context can help accelerate the digital transformation of the industries HCP engagement approach.

Best-practices and Learnings from Digital Natives	Lifesciences Ecosystem Context
Focus on differentiated HCP experience	Physicians have different interaction points, interests, and requirements including clinical content, CMEs, studies, samples, copay coupons, patient counseling material, etc. and hence differentiated experience enables engagement.
Volume and variety of data	Pharma has access to multi-dimensional physician data in terms of demography, preferences, prescription patterns, patient/payer mix profiles via claims, digital affinity to micro-segment physicians, and uncover preferences, behaviors, and personalized needs.
HCP/Customer Journey management and personalization	Advanced analytics and ML-based approaches can leverage the available data to predict intents, recommend interventions, and seamlessly deliver them via physician engagement platform and processes.
Omnichannel execution	Multi-channel interaction provides a foundation platform for delivering these experiences across digital as well as non-digital channels.
Measure, Learn & Improve	A/B testing driven digital engagement experimentation anchored on performance-driven, yet responsive targeting strategies.

To accelerate their digital transformation journey, biopharma companies need to inculcate these best practices into their HCP digital marketing capability. An integrated Digital Engagement solution will help biopharma companies create and deliver omnichannel personalized experiences for HCPs, by enabling real-time AI/ML-driven next-best-action recommendations and precision targeting strategies based on their preference and intents.

COVID pandemic is an unprecedented global event, which will radically alter our behaviors, expectations, and interactions. Earlier rules of engagement are now getting irrelevant at a pace that is faster than ever before. To maintain(and grow) their share of voice and engagement with HCPs, bio-pharma organizations can no longer afford to follow the “digital-addon” approach. They have to fundamentally re-design their HCP engagement framework, as a Digital-driven strategy, $to stay relevant, to stay ahead and keep growing.

[1] Sermo COVID-19 Survey Apr, 2020
[2] Sermo COVID-19 Survey Apr, 2020

While the reckless overextension of credit lines by lenders and banks was the root cause of the financial crisis of 2007-09 and it had the US primarily as its central point, this time the financial crisis has been caused by a virus with rapidly evolving geographical centers and covering almost the entire world. The banks though are in a catch 22 situation, they need to support the government’s lending and loan relief measures while also maintaining low credit loss rates and enough capital provisioning for their balance sheet. Effective risk management and credit policy decisioning was never as challenging for the banks as it is now in the post covid-19 world.

COVID-19 implications and challenges for banks and lending institutions

Sudden shift in risk profile of retail and commercial customers – The surge in unemployment, deteriorated cash flow for businesses, etc has led to a sudden shift in the credit profile of customers. The data that banks used to leverage before COVID might not provide an accurate picture of the consumer’s risk profile in the current times.

Narrow window of opportunity to re-define credit policies – Bank’s credit policies in terms of origination, existing customer management, collections, etc have been designed over years with a lot of rigor, market tests, design and application of credit risk models and scorecards, etc. The coronavirus has caught the bankers and Chief Risk Officers by surprise and there is a narrow window of opportunity to make changes in existing models and risk strategies. While a lot of banks had built a practice of stress testing for unfavorable macroeconomic scenarios, the pace and impact of coronavirus have been unprecedented. This requires immediate response from the banks to mitigate the expected risks.

Government relief programs like payment moratoriums – The introduction of payment holidays and moratorium programs are effective to take some burden off consumers but prevent the banks from understanding high risk customers as there is no measure of delinquency that banks can capture from existing data.

Four-point action plan and strategy to navigate through the COVID-19 crisis

Banks will need to go back to the drawing board, re-imagine their credit strategy and put in accelerated war room efforts to leverage data and create personalized risk decisioning policies. Based on Incedo’s experience of supporting some of the mid-tier banks in the US for post COVID risk management, we believe the following could help banks and lenders make a fast shift to enhanced credit policies and mitigate portfolio risk

Covid situational risk assessment – As a starting point, Risk managers should identify the distress indicators that capture the situational risk posed post Covid-19. These indicators could be a firsthand source of customer’s situational risk (e.g. drop in payroll income) or surrogate variables like higher utilization or use of cash advance facility on credit card etc. Banks would need to leverage a combination of internal and external parameters, such as industry, geography, employment type, customer payment behavior, etc. to quantify COVID based situational risk for a given customer.
Early warning alerts & heuristic risk scores based on a recent behavioral shift in customer’s risk profile – A sudden change in the financial distress signals should be captured to create automated alerts at the customer level, this in combination with a historical risk of the customer (pre-COVID) should go as a key input variable into the overall risk decisioning process. The Early warning system should issue alerts, alerting the credit risk system of abnormal fluctuations and potential stress prone behavior for a given account.
Executive Command Centre for COVID Risk Monitoring – The re-defined heuristic customer risk scores should be leveraged to quantify the overall risk exposure for the bank post COVID. Banks need to monitor the rapidly changing credit behavior of customers on a periodic basis and identify key opportunities. The rapid risk monitoring based command center should focus on risk across the customer lifecycle and various risk strategies and help provide answers to some of the following questions of the bank’s management team
- What is overall current risk exposure and forecasted risk exposure over short term period?
- How has the overall credit quality of existing customer base changed, are there any patterns across different credit product portfolios?
- What type of customers are using payment moratoriums, what is the expected risk of default of such customer segments?
- Quantification of the drop in income estimates at an overall portfolio level and how it could affect other credit interventions?
- What models are witnessing significant deterioration in performance and may need re-calibration as high priority models?
Personalized credit interventions strategy (Whom to Defend vs Grow vs Economize vs Exit) – To manage credit risk while optimizing the customer experience, banks should use data driven personalized interventions framework of Defend, Grow, Economize & Exit. Using customer’s historical risk, post COVID risk and potential future value-based framework, optimal credit intervention strategy should be carved out. This framework should enable banks to help customers with short term liquidity crunch through government relief programs, bank loan re-negotiation and settlement offers while building a better portfolio by sourcing credit to creditworthy customers in the current low interest rate environment.

The execution of the above-mentioned action plan should help banks to not only mitigate the expected surge in credit risk but also enable a competitive advantage as we move towards the new-normal. The rapid credit decisioning should be backed with more informed decision making and on an ongoing basis, the framework should be fine-tuned to reflect the real pattern of delinquencies.

Incedo with its team of credit risk experts and data scientists has enabled setting up the post COVID early monitoring system, heuristic post COVID risk scores and COVID command center for a couple of mid-tier US based banks over a period of last few weeks.

Learn more about how Incedo can help you with credit risk management.

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