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

  1. 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.

    Covid-situational-risk-assessment
  2. 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.

    early-warning-alerts-heuristic-risk-scores
  3. 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?executive-command-centre-for-COVID-risk-monitoring
  4. 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.

personalized-credit-interventions-strategy

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.

The magnitude of the spread of the COVID-19 pandemic has forced the world to come to a virtual halt, with a sharp negative impact on the economies worldwide. The last few weeks have seen one of the most brutal global equity collapse, spike in unemployment numbers, and negative GDP forecasts. With the crisis posing a major systemic financial risk, effective credit risk management in these times is the key imperative for the banks, fintech and lending institutions.

Expected spike in delinquencies and credit losses post COVID-19

The creditworthiness of banking customers for both retail and commercial portfolios has decreased drastically due to the sudden negative impact on their employment and income. In case of continuation of the epidemic for a longer-term period, the scenarios in terms of defaults and credit losses for banks could potentially be much higher than as observed in the global financial crisis of 2008.
expected spike in delinquencies and credit losses post covid-19

Need for an up-to-date, agile and analytics driven credit decisioning framework:

The existing models that banks rely upon simply did not account for such a ‘black swan event’. The credit decisioning framework for banks based on existing risk models and business criteria would be suboptimal in assessing customer risk, putting the reliability of these models in doubt. There is an immediate need for banks to adapt new credit lending framework to quickly and effectively identify risks and make changes in their credit policies

Incedo’s risk management framework for the post COVID-19 world

To address the challenges thrown up by the COVID-19, it is important to assess the short, medium and long-term impact on bank’s credit portfolio risk and define a clear roadmap as a strategic response focusing on changes to risk management methodologies, credit risk models and existing policies.

We propose a six-step framework for banks and lending institutions which comprises of the following approaches.

Roadmap-for-post-Covid-credit-risk-management

  1. COVID Risk Assessment & Early monitoring Systems

Banks and lending institutions should focus on control room efforts and carry out a rapid re-assessment of customer and portfolio risk. This should be based on COVID situational risk distress indicators and anomalies observed in customer behaviour post COVID-19. As an example, sudden spike in utilization for a customer, less or no credit of salary in payroll account, usage of cash advance facility by transactor persona could potentially be examples of increasing situational risk for a given customer. In the absence of real delinquencies (due to moratorium or payment holidays facility), such triggers should enable banks to understand customer’s changing profiles and create automated alerts around the same.

COVID-risk-assessment-and-early-monitoring-systems

  1. Credit risk tightening measures

Whether you are a chief risk officer of a bank or a credit risk practitioner, by now you would have heard many times that all your previous credit risk models and scorecards would not hold and validate any longer. While that is true, it has also been observed that directionally most of these models would still rank order with only a few exceptions. These exceptions or business over-rides can be captured through early monitoring signals and overlaid on top of existing risk scores as a very short term plan. Customers with a low risk score and situational risk deterioration based on early monitoring triggers are the segments where credit policy needs to be tightened. As the delinquencies start getting captured, banks should re-create these models and identify the most optimal cutoffs for credit decisioning.

credit-risk-tightening-measures

  1. Personalized Credit Interventions

There are still customers with superior credit worthiness waiting to borrow for their financial needs. It is very important for banks to discern such customers from those that have a low ability to payback. To do this, banks require personalized interventions to reduce risk exposure while ensuring an optimal customer experience through data-driven personalized interventions. Banks need to help customers with liquidity crunch through Government relief programs, bank loan re-negotiation, and settlement offers while building a better portfolio by sourcing credit to ‘good’ customers in the current low rate environment.

  1. Models Re-design and Re-Calibration

A wait and watch approach for the next 2-3 months period to understand the shifts in customer profile and behavior is a precursor before re-designing the existing models. This would enable banks to better understand the effect of the crisis on customer profiles and make intelligent scenarios around the future trend for delinquencies. There would be a need to re-calibrate or re-design the existing models. Periodic re-monitoring of new models would be a must, given the expected economic volatility for at least next 6-12 months period.

  1. Model Risk Management through Risk Governance and Rapid Model Monitoring

There is an urgent need for banks to identify and quantify the risks emerging due to the use of historical credit risk models and scorecards through Model monitoring. While the risk associated with credit products has increased, the delinquencies have not yet started getting captured in the bank’s database due to the payment holiday period facility introduced by govt’s of most of the countries. In such a situation, it is critical to design risk governance rules for new models that may not have information related to dependent variables (e.g. delinquency) captured accurately.

  1. Portfolio Stress Tests aligned with dynamic macro economic scenarios

Banks and lending institutions need to leverage and further build on their stress testing practice by running dynamic macro-economic scenarios on a periodic basis. The stress testing practice has enabled banks in the US to improve their capital provisioning and the COVID crisis should further enable banks across the geographies to use the stress tests to guide their future roadmap depending on how their financials would fare under different scenarios and take remedial actions.

The execution of the above-mentioned framework should ensure that banks and fintech’s are able to respond to immediate priorities to protect the downside while emerging stronger as we enter the new normal of the credit lending marketplace.

Incedo is at the forefront of helping organizations transform the risk management post COVID-19 through advanced analytics, while supporting broader efforts to maximize risk adjusted returns.

Our 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 centre for a couple of mid-tier US based banks over a period of last few weeks.

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

The wealth management industry has gone through major changes in the past few years.

The amount of investable wealth among U.S. households has increased tremendously over the past few years and will be changing hands with wealth passed over to millennial. Over the next 25 years, Cerulli has estimated that $31 trillion will be passed on to Generation X households, while $22 trillion will be passed on to Millennials [1].  With two diverse trends – growing wealth in the hands of a younger demographic and aging investors primarily the baby boomers having complex financial goals ranging from retirement planning to long term medical care, the demand for financial advisors has also grown simultaneously. Independent financial advisors have continued to grow in terms of revenue and assets under management. The trend should continue as advisors can enjoy the flexibility and opportunity for higher income with fewer cuts to wirehouses and broker dealers. The major drawback of going independent is the lack of support for back office and administrative operations. This is where turnkey asset management providers come into the picture to help provide advisors the necessary support.

TAMPs have been helping advisors focus their attention on client needs while taking over back office and administrative support activities, including client onboarding, asset transfers, trade execution, portfolio management, trust accounting, proposal generation, and performance reporting.

Trends impacting advisors and TAMPs:

  1. Financial advisers are experiencing an increase in demand by young professionals. These are the HENRYs (High earners not rich yet) segment with lower range assets but would like to start investing and are looking for financial guidance to keep them on the right track towards their long-term goals. [5]
  2. “One of the biggest challenges facing investment advisory firms today is disintermediation. People can invest by themselves rather than hiring an investment professional to manage their money”. Advisors need to provide clients with an experience which is custom for their needs, shows value add and helps them invest strategically.
  3. Technology is redefining the advisor-client experience in multiple ways. Clients now want to have access to their portfolios and performance instantly which means advisors need to share on-demand requests with a low turnaround time.
  4. Clients expect personalized and custom services suitable to their individual risk profile and future goals. While tech savvy investors look for sophisticated digital systems, they also see value in the attention and financial experience of advisors to help them build a smart investment portfolio. As a result, advisors’ expectations are increasingly focused on technology & better investment management
  5. ‘Holistic financial planning,’ which goes beyond client set up and onboarding, changing investment strategy basis new life events, addressing multiple life goals are essential for advisor success. Advisors are therefore, looking for digital platforms which will enable them to service these needs. For example, a portfolio simulation which will help clients design different investment scenarios and view the impact of those changes on their goals can be hugely beneficial for advisors.

Strategy to address market trends:

  1. Reimagining the client experience: To meet client expectations of personal and customized investment strategy, TAMPs need to provide advisors with digital solutions enabling them to walk clients through risk analysis, goal setup and investment strategy definition in a simple yet effective manner. Two technology offerings are key to successfully optimize the client experience – investor portal and smart portfolio generation platform. Clients value access to their portfolio and look for information beyond quarterly performance reports. An investor portal providing a 360 degree of the client accounts, progress towards goals, investment strategies and performance has become a basic requirement for many clients and therefore, advisors. A sophisticated portfolio selection tool which will recommend investment strategies basis the clients’ stage of life, their goals, major events such as receiving inheritance, retirement, marriage and their attitude towards risk and market changes will enable advisors to provide a hybrid model with a smart platform and human touch
  2. Optimize advisor performance: The key success metric for TAMPs is growth in AUM, which is dependent on the success of advisors and their ability to acquire new clients and retain existing ones. Advisor performance analytics is therefore gaining traction and becoming increasingly relevant. Firms must leverage data analytics to derive insights from best performing advisors and provide the next best action to help them better collaborate with clients. To retain and bring in new advisors, TAMPs should review advisor experience metrics, assess CSAT wr.t. technology & operations services and continue to improve the experience through simplified back office processes and technology solutions.
  3. Drive profitability through efficient operations: While technology platforms enable advisors to grow, efficient back office support is necessary to help independent advisors survive. Adding services to the operations portfolio will provide immense value add for advisors. While billing, trade management, statements generation are core activities, additional services such as sleeve level reporting, white labelling, custom proposal generation, trust accounting, tax loss harvesting, automated rebalancing, account aggregation will help acquire more advisors. A key focus area for TAMPs should be to minimize operations & compliance risk as meeting compliance requirements is a top priority for advisors. Using automation to improve the speed and accuracy of transactional processes helps reduce costs and improve accuracy.

Sources:

  1. Cerulli Associates, Federal Reserve, U.S. Census Bureau, Internal Revenue Service, Bureau of Labor Statistics, and the Social Security Administration
  2. A Year of Tremendous Growth for RIAs
  3. https://www.cnbc.com/2019/10/17/these-are-the-changes-and-challenges-keeping-top-advisors-up-at-night.html?__source=sharebar|twitter&par=sharebar
  4. https://www.cnbc.com/2019/10/14/technology-is-redefining-that-client-financial-advisor-relationship.html?__source=sharebar|twitter&par=sharebar
  5. https://www.thestreet.com/personal-finance/financial-planners-see-growing-demand-from-younger-prospects-14772572

Over the past two months, COVID-19 has not only created a global health crisis but also led to socio economic disruption and affected major industry sectors, including healthcare, banking, insurance, capital markets and so on.

Wealth management is one of the vulnerable sectors with highly correlated revenues to capital market performance and has already started experiencing loss in revenue and growth. The stock market response to the COVID-19 pandemic has been panic driven and volatile and could continue to be so until the spread of the virus is contained. With the economic data likely to worsen in the coming months, stock markets could experience another round of correction.

As a result, firms have initially struggled and are now implementing plans to reduce costs, assess spending, with continued efforts to tackle extremely high trade volumes and keep critical processes running. Most firms have now dealt with the initial priorities to ensure large scale business continuity and set up the majority of the workforce to work remotely. These firms are now working to identify data and information security risks and reprioritize organization strategies and projects.

There are a few firms that are yielding benefits of prior investments in digital transformation, automation and infosec who are slightly ahead in the digital maturity curve while others are just starting out to plan and strategize their digital journey for  the near future.

From our experience, we believe there are four key themes shaping up during this crisis which will help wealth management firms stay resilient:

  1. Focus on cost reduction and rationalization: To tackle market volatility, there is an increased focus on optimizing costs and improving operational efficiency. With a growing volume of business transactions, deployment of tactical automation solutions to automate trade processing and compliance reporting will embed the much needed flexibility and improve productivity. Outsourcing additional processes for short to medium term will also help address the increase in workload without huge cost investments. On the technology front, leveraging cloud solutions would be a quick win to reduce fixed costs immediately.
  2. Prioritize risk and data security: Given millions of resources are working remotely, companies will have to revisit cybersecurity best practices and enhance/upgrade systems to protect from unauthorized access, phishing scams, etc. With unsecured channels and networks for remote employees, wealth management firms will also need to reassess access to applications depending on criticality due to the increasing threat of cybersecurity. Adoption of multi-factor authentication and enhancing security incident management protocols would be vital in maintaining data security.
  3. Continue to focus on Digital Transformation: Firms need to double down at their digital transformation practice to defend their core business and emerge as a winner in this new normal. Digital analytics is critical for companies to refine their portfolio strategy, help automate critical processes through usage patterns, strengthen market research and insights to better communicate with advisors, broker dealers and investors. The significance of omnichannel and well-designed advisor & investor portals could have never been higher. Simple and intuitive portals will help communicate account/portfolio performance and help stakeholders make data, transaction requests faster and understand how they are being impacted in real time. It’s critical to harness the data across the web, mobile, branches, CRM to make sure the best of the experience can be provided to clients and advisors.
  4. Enhance IT resiliency: Most firms were unprepared for a crisis of this magnitude, given its unprecedented nature. While on the one hand, businesses have managed to get their workforces set up remotely, it is critical that they continue to assess the impact of network traffic, volumes,  on the infrastructure. They should also prepare and update plans to address security breaches, network breakdowns, and critical resource unavailability in a proactive manner.

In spite of the downfalls, every crisis helps businesses realize their underlying strengths and helps them define their strategy roadmap for the next journey. We strongly believe that investments in operational efficiencies, digital transformation and customer experience optimization while continuing to work on data security and BCP will be the key pillars of running a resilient business during this crisis. They will continue to remain important in the ‘new normal’ that will emerge post the pandemic as well.

Enabling personalization at scale for consumer banks

Consider the case of a representative customer we’ll call Alex. Alex buys an iphone on his credit card and with this purchase, ends up utilizing around 90% of his credit limit. He gets a sms from his bank within minutes after his purchase, for a credit limit increase offer. With some more expenses expected in coming few days, Alex calls up the customer care and during discussions with call centre rep, is also given an option to convert his purchases into an EMI with attractive interest rate. Alex ends up opting for both Credit limit increase and EMI loan-on-card. What began as a single high-ticket item purchase, ended up becoming a much more engaging experience for Alex.

Welcome to the new world of data science enabled personalization. In the above case, Alex’s bank found that Alex (a credit worthy customer with good credit score) is in a need of extra credit and facilitated the next best action of offering credit limit increase through SMS channel. Not only that, bank’s data science algorithms also defined a price point for EMI loan-on-card to improve Alex’s chances of taking EMI loan and pushed that offer through call centre CRM. Such data-based personalized marketing strategy is the final goalpost for consumer banks to help enable strong customer experience, reduce churn and improve bottom line profitability.

While a very few digital natives and fintech players like Alex’s bank have been able to provide right one-to-one experience to their customer base, rest of the organizations still have a huge opportunity to leverage advanced data science practices and provide personalization on scale for their prospects and existing customers.
Based on our experience of working with some of the consumer banks in US and Latam markets, Incedo has developed a framework called Data Science Maturity Model for Consumer Banking Personalization. The framework describes the maturity levels that currently exist within data science teams for marketing personalization across the ecosystem.

In this post, we describe the Data Science Maturity Model and share the key challenges that are preventing banks from stepping up in their personalization journey to become hyper relevant to their customers.

Stages of Maturity – Data Science Personalization Model for Consumer Banking

Based on our industry experience, it has been seen that banks tend to fall into four main stages of data science based banking personalization maturity

Stages of Maturity – Data Science Personalization Model for Consumer Banking

Implementation Complexity

Level 1 : Product Centric

This is ground zero & is an approach used by most of the consumer banking institutions. The goal here is to look at analytics with a siloed product level focus. In context of a banking firm, products may include credit card, personal loan, mortgage etc.

The product heads typically focus on marketing-based strategies leveraging product propensity segmentation or models. The customers who fall in top deciles of each product model end up getting bombarded with offers while there are no contacts with prospects appearing in low deciles of these models. Since there is no focus on customer profitability or life time value, this approach is not optimal from both revenue maximization and customer experience point of view.

Level 2 : Customer X Product Centric

In this stage, firms look at customer management strategies to acquire, cross sell & upsell prospects. The focus is to look at product grid for each customer and identify which product would maximize firm’s profitability, while ensuring good chances of customer to take up the product. Consider an example where a customer has similar probability to take up both Product A and Product B and decision around next best product needs to be taken. In this case, the product which maximizes life time value for client is solicited to the customer.
Based on our experience of enabling customer centric product level recommendations for banks, the move from Level 1 to Level 2 of personalization can lead to incremental bottom-line impact of 10-15%, depending on the existing targeting framework being used at the organization.

Level 3: Customer X Product X Offer Centric

A personal loan offer with an APR of 12% vs APR of 18% would typically have different response propensities & profitability for the bank. For a price sensitive customer with good credit history, response rate would be much higher at 12% offer while bank’s margin & revenue would be higher for 18%. At this stage of personalization, decisioning models (response & value) are built for each Customer X Product X Offer permutation & business simulation & optimization exercises are carried out to identify optimal product & offer for each eligible customer. The final decisioning is based on what PnL KPIs business would want to maximize (e.g. # bookings, $ sales, $revenue etc)
In recent cases where we designed and implemented offer & pricing personalization strategy for our clients, there was an increase of ~10% in terms of revenue of the overall marketing program, in comparison to Level 2.

Level 4: Omnichannel Customer X Product X Offer strategy

The final stage of banking personalization journey involves focus on optimal contact strategy in terms of preferred channel of contact, frequency of contacts etc while also ensuring that right offer is selected for the customer. The data science engine would typically run the simulations based on different data science models to arrive at a personalized strategy for each customer in terms of product, offer & channel contacts, the optimal personalization is then enabled & fulfilled through front end operations teams (call centre, email etc). The right offer through right channel & creative helps improve customer experience & maximize bank’s profitability. While this stage helps maximize the incremental impact of data science initiatives for an organization, it comes with a trade-off in terms of high complexity of implementation.

What’s holding back the consumer banks from moving up the personalization analytics maturity curve?

If the incremental value gained through data science based personalization is so substantial and clear, why is it that not all the banks are already monetizing and achieving impact with it ?
The reason is that most of them continue to struggle with fundamental issues that prevent them from leveraging data science to drive the most optimal & personalized customer experience. These challenges span across data, technology and organizational areas and have been summarized below.

1. Lacking Data Quality & Technology Infrastructure
The first step and a must do in order to create value from data science is accessing all the information that is relevant to a given problem. This entails capturing and generation of data as a first step followed by integration of large stores of data from various sources.

While there are big data platforms and cloud-based services available to store massive amounts of data, the companies are still facing internal barriers in terms of data capture and quality of information. This in addition to long turnaround times to make a switch from legacy technology platforms is acting as a major bottleneck for organizations to build an accurate customer level data repository, which is a precursor to leveraging the state-of-the-art data science tools & algorithms.

2. Insufficient depth in Data Science Capabilities
Personalization is about treating each individual customer as a population of one and designing targeting strategies by leveraging features that encapsulate customer behavior in terms of product usage, spend habits, demographics, interactions across channels, customer journey at a point of time etc. Building such data science based solutions not only requires deep understanding of the sophisticated machine learning & deep learning algorithms but also involves clear understanding of the problem and running a series of business optimizations, before the final recommendations can be implemented in market.

In our experience, we found that most of the consumer banking organizations either don’t have sufficient depth in terms of data science talent and capabilities or have narrow focus on tools and techniques without clear roadmap on pragmatic implementation of data science solutions for driving business impact.

3. Siloed Organizational Structure
The operating model of data science organization for majority of banking institutions comprises of different data science teams operating as islands and tagged to each business unit.
As an example, during Incedo’s data science engagement with one of our client, we found that different data science teams were aligned to each of the product units (credit card, auto loan, personal loan etc) which prevented the firm from designing customer level omnichannel strategy across the product portfolio. The siloed operating model for data science prevents businesses from realizing best possible value from their analytics organization.

Personalized decisioning and targeting of products & offers is a critical imperative for consumer banking firms to operate in the competitive digital environment. To get there, organizations need to identify where they are currently in terms of data science maturity model and should create a roadmap to improve their personalization capabilities.

No matter what maturity stage you are in your data science based personalization journey, our team of experts can help you design and implement data science solutions that create bottom-line impact and provide seamless & wow experience for your prospects and customer base.

Over next few weeks, we would plan to explore and share our perspective in detail, on how to get around the three key challenges in personalization journey. Stay tuned!

Quantum Computing is the use of quantum-mechanical phenomena such as superposition and entanglement to perform computation. Quantum computers perform calculations based on the probability of an object’s state before it is measured – instead of just 1s or 0s – which means they have the potential to process more data compared to classical computers.

In a world of constant and extensive technology disruption, with organizations engaged in a battle for survival, the urgency to digitally transform is well understood by almost every large enterprise.

That everyone is trying to go digital is well established. Yet organizations continue to grapple with achieving breakthrough business impact from digital transformation programs.

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