vandana - Incedo Inc.

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.

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.

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.

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.

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.

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.

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.

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:

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]
“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.
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.
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
‘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:

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
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.
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:

Cerulli Associates, Federal Reserve, U.S. Census Bureau, Internal Revenue Service, Bureau of Labor Statistics, and the Social Security Administration
A Year of Tremendous Growth for RIAs
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
https://www.cnbc.com/2019/10/14/technology-is-redefining-that-client-financial-advisor-relationship.html?__source=sharebar|twitter&par =sharebar
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:

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.
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.
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.
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.

The Covid -19 pandemic continues to disrupt the Pharma industry. As uncertainty around the pandemic lingers and refuses to go away, Pharma leaders are facing extraordinary challenges due to the following forces at work:

Rapid shift in demand of drugs due to the impact of Covid-19 – The pandemic has impacted the demand of drugs for various therapeutic areas differently. There is an unprecedented surge in demand for drugs which are being considered as treatment candidates for Covid-19 e.g Remdesivir (Gilead), Actemra (GNE) and Kevzara (Regeneron). There has also been a significant upsurge in demand for symptomatic medicines like antivirals, pain medications and ICU medicines which are used for managing complications from Covid-19. On the other hand, delays in elective surgeries and non-essential treatments have led to huge drop in Rx for many categories, and a rise of product switching in favor of self administered drugs

Geographical risk due to Covid-19 changing very quickly – After weeks of shutdown, some countries and states are cautiously reopening their economy. As regions open up, there are new emerging hotspots which can modify the density of cases, and hence the downstream impact on key decisions for Pharma Cos like inventory allocation for treatment therapies, supplier management and execution of clinical trials. Given the rapidly evolving dynamics with Covid-19, companies need to ensure that they are using the most updated data and case forecasts for decision making

Pharma forecasts are broken – For an Industry which relies very heavily on forecasting, the historic data on which all forecasting, planning and distribution systems are built on has changed. Many of the previous signals used for forecasting like seasonal patterns, events, channel characteristics and patient behavior might not hold true going forward. There are new behaviors like hoarding, preference for self administered drugs and movement to telehealth which challenge pre-existing assumptions. Forecasters need to factor in this “black swan” scenario into their assumptions, and the geographical risk of cases would be one of the key factors impacting Pharma KPIs

Given this scale of disruption, how can Pharma companies solve this?

Unprecedented problems can still be solved with conventional solutions. With the right tools, Data science can provide much needed clarity, direction and guidance on what is happening now , and what is expected to happen ahead. We propose a 5 step approach with a Covid-19 Control Room for Pharma companies which composes of the following components.

Covid-19 geographic risk assessment – Assess how the Covid-19 epidemic would play out with estimation of Covid-19 cases at Country, State and County level. There are multiple sources of forecasts like IHME, Northeastern University, Columbia University or you can build custom SIER Models. Models are only as good as their assumptions, so it is advisable to look at forecasts from multiple models to capture the possible range of outcomes for assessment of the geographical risk. A dashboard view like the one below, with the ability to customize the forecast would be a foundation for the Covid-19 Control room. This base estimation of geographical risk can be used to model scenarios for a range of decisions e.g inventory allocation, supplier risk and clinical trial management being some of them
Segmentation of drugs based on categories of consumption – If you observe the pattern of how demand is getting disrupted across therapeutic areas, there are 3 key demand archetypes that would emerge.
- Direct impact – For drugs which are in late stage clinical trials for treatment of Covid-19 e.g Actemra (GNE), Kevzara (Regeneron), Lopinavir+Ritonavir (AbbVie) – effects of hoarding or upsurge are leading to more than 5X increase in sales, with demand quickly outstripping supply. This has already led to shortages. Remdesivir which received emergency use authorization by FDA might be in shortage for a long time – Gilead reported there’s only enough of it for 200k patients around the world
- Secondary impact – For drugs which help in symptom management e.g pain/anaesthetic drugs like Paracetamol & Ibuprofen, antivirals like Rapivab and respiratory drugs have seen a huge uptick in demand e.g 91% increase in Paracetamol, 27% in Beclometasone, 23% increase in Salbutamol in the first week of Mar’20 compared to Mar’19. There are also a class of drugs used for Covid-19 complication management e.g ICU drugs like Epinephrine, Fentanyl and Oxycodone which have seen demand surges and reported shortages
- Negative or no impact – For some categories, demand has fallen sharply. In office administration volumes show huge drop in demand including certain categories of prescription drugs e.g -45% Rx for Pediatric Antibiotics. There are shifts based on mode of administration e.g IV administered oncology therapies show decreased demand relative to oral therapies
Calibrate demand sensing for each demand archetype – Understanding the demand archetype of drugs in the company’s portfolio would enable forecasters to calibrate the demand post disruption and improve the accuracy of their forecasts. One of the key signals of drug demand at the distributor level is the geographic risk. For some like Actemra and Kevzara, the increase in demand would be directly proportional to the number of cases with Covid-19 in any region. E.g we know that Kevzara is a treatment candidate for patients with severe pneumonia due to Covid-19. Signals like number of expected cases, admission rates, patient demographics & access to the drug can be used to derive an accurate estimate of the demand for the drug. Similar analysis is needed based on demand archetype to calibrate forecasting techniques for other therapeutic areas given this ‘structural break’ in historic time series data due to Covid-19
Develop strategies for short, medium and long term – Once you have a sense of impact on the therapeutic area, organize your efforts for the Covid-19 Control room which would provide a perspective on short term ‘Crisis management’, medium term ‘Risk management’ and long term ‘Restoration to normal’ initiatives. For Supply chains, the initiatives would be:
- Short term Crisis Management – Inventory risk assessment & allocation
- Medium term Risk management – exploring options for ramping up production, managing supplier risk and reducing lead time
- Long term Restoration to Normal – capacity planning, planning for recurring cycles of pandemic
For example, here is an illustration of Inventory Risk assessment with key insights on Inventory Allocation at county level for a demand archetype with direct Covid impact. An inventory risk assessment and allocation solution would compute the mismatch between demand and supply – measured by the ‘shortfall from required DoH’ to surface alerts. This would ensure that inventory allocation is optimal – precious drugs are sent to the critical locations and hospitals who need it most.
Build early warning indicators – Covid-19 infections, as some expect, might stay and relapse long into the future – even after the first wave. Machine learning can be used to constantly analyze and correlate parameters like case rate, death rate and growths with anomaly detection systems which detect shifts in cases and identify emerging hotspots of infection. This will help companies quickly recalibrate decisions e.g here is a view of how an autonomous Anomaly detection system for the Covid Control room is built at country, state and county levels enabling decision makers to zoom in and out to identify hotspots quickly.

As we have all experienced, every day is a new unprecedented chapter in this outbreak of Covid-19. Strategies leveraging data and tools at our disposal can help Pharma companies win the battle against this pandemic. Companies that execute on these strategies will have a clearer view of what is expected to happen, and hence better prepared to face the challenges which lie ahead.

This Article is part 2 in the series – ‘Managing Pharma Supply Chains in times of Covid-19‘

For more insights on how Pharma companies can Optimize their Supply chains, please click here.

FDA Guidelines on Real World Data and Real-World Evidence

“As the breadth and reliability of RWE increases, so do the opportunities for FDA to make use of this information”, noted Scott Gottlieb, former FDA Commissioner National Academies of Science, Engineering, and Medicine while examining the impact of RWE on medical product development.

FDA has a long history of using RWE to monitor and evaluate the safety of drug products after they are approved. Real world data traditionally come from a variety of sources such as data derived from electronic health records (EHRs), medical claims and billing data, data from product and disease registries, patient-generated data, including from in-home-use settings, and even data from mobile devices that can inform on health status. With such ever-increasing reliability of real world data, FDA published its Framework for FDA’s Real World Evidence program. It laid out a framework for evaluating the potential use of real-world evidence (RWE) to help support the approval of a new indication for a drug already approved under section 505(c) of the FD&C Act or to help support or satisfy drug post-approval study requirements.

Further, the May 2019 draft guidelines, “Submitting Documents Using Real-World Data (RWD) and Real-World Evidence (RWE) to FDA for Drugs and Biologics,” encourage sponsors using RWD to generate RWE as part of a regulatory submission for investigational new drug applications (INDs), new drug applications (NDAs) and biologics license applications (BLAs).

These developments are growing acknowledgement by FDA on the need for Real-World Evidence across RCTs, single arm trials and observational studies, to enhance clinical research and support regulatory decision making.

Are Pharma and CROs ready ?

FDA has made RWD and RWE a “top strategic priority”. Medical Research is at a turning point – with an abundance of real-world data from a wide variety of sources ranging from EHRs to wearables like Fitbits delivering terabytes of data, healthcare practitioners are pushing ahead on delivering personalized care, while acknowledgingthe value of real world data and evidence.

It’s a redefining moment for Lifesciences industry – the professionals across the industry understand that RWD will transform not only clinical development process but also commercialization and reimbursement decisions. However, Life Sciences – Pharma, Biotechs, CROs – are yet to widely adopt RWD insights in an institutional way because many are unsure of the road forward and are facing several roadblocks.

There are challenges such as:

Inconsistent RWE data collection and quality while increasing complications due to new data sources, resulting in data fragmentation.

In Lifesciences industry having data has never been a problem, however, the ability to stitch together a robust patient-journey data has been complicated. Once the patient health journey data is complete, it allows researchers to compare interventions and outcomes more meaningfully. With this the objective to fully understand the impact of different clinical options can be fulfilled. More importantly, the insights give researchers an ability to understand patient’s challenges and assess how their medical products perform when the patient need is the highest.

RWD researchers today typically use Electronic Health Record, Health Insurance Claims and Population Health sources of aggregated data for their research. There is a growing desire to leverage several new sources of patient data to make evidence generation more robust, insightful, and richer. Genomic data, biomarkers and digital data i.e. data generated from mobile devices, wearables, health apps or other biometric devices – are topmost in the priority order across organizations and researchers for real world insight generation.

However, organizations usually encounter challenges in stitching together the patient journey across these varied RWD data sets, due to their inherent nuances and complications.

Lack of robust internal resources with the needed RWD experience and expertise.

The current nature of RWD initiatives or studies is very bespoke within lifesciences organizations. The typical approach has been to execute each study in a silo, usually dependent upon a few researchers and biostatisticians. Such an approach has led to significant gap in terms of demand for pre and post commercialization real world analytics, and the capabilities of the organization to fulfill the demand.

With hurdles such as lack of limited RWE skilled resource pool, poor knowledge sharing, lack of standard methodologies and next to no reusability, this gap continues to widen.

Need for technology to manage and analyze data and provide RWE.

With the exponentially growing complexity and volume of different emerging RWD data sources,Lifescience and Pharma organization are seen to be insufficient for analysis and evidence generation. For example, with the increasing use of wearables and biometric devices, digitally patient reported data is becoming mainstream for clinical drug trials, while the traditional data platforms are struggling to process this real-time, streaming source of patient health information. Similarly, intelligent insight generation from EHR data is something which is beyond capabilities of current systems, as it requires new-age AI and NLP capabilities to process text data which is missing in traditional systems.

Advanced data and AI/ML driven computing technologies are critical to aggregate consistent and robust patient-journey data from these RWD sources.

Strategy for institutionalizing Real-World Data and Evidence Generation

The role of real world evidence in drug development and post-commercialization evidence monitoring is really turning the corner. So far, only payers were demanding evidence as part of the access and reimbursement process; but with FDA putting its full weight behind acknowledging it as a critical part of its regulatory framework, it right time for Pharma and CROs to build their real world data capabilities at the institutional level.

Evolving RWE as a core institutional capability and to give it necessary organizational focus, Pharma and CROs has to invest in and build out centralized “RWE center of excellence”, with a federated ecosystem of evidence generation.

The RWE center of excellence must focus on developing core capabilities around RWE data acquisitions, standards, technology and processes.
- Develops and enforces RWE data collection and quality standards
- Acquires, develops best-in-class Big data (high complexity, structured, unstructured, streaming) processing and management capabilities for enterprise RWE data management
- Design and implements standard analytics methodologies to ensure consistency in quality of evidence generated across the organization
- Institutionalizes RWE knowledge base via structured training and knowledge management, supporting the rapid ramp-up of enterprise RWE skilled resources
- Develops standards and SOPs across RWE processes and evidence reporting
- Drives execution of strategic and highly critical evidence studies

The broader federated ecosystem – regional, therapeutic area, product line-based teams – focus on study design and execution as well as developing partnerships and alliances.
- Execute critical RWE studies for their respective regions, therapeutic areas or product portfolios
- Drives local, regional or therapeutics areas partnerships with RWE data owners, peers, providers and other health industry stakeholders

Such a “core” Center of Excellence integrated with “federated” evidence generation strategy, is critical to bring the required scale to overall RWE generation and enablement by marrying COE driven standardization with the flexibility to meet growing specific RWE needs across the organization.

Patient Health Outcomes as north star of Healthcare ecosystem

Changing healthcare environment is resulting in a global consensus that all medical products and services must provide evidence of value – improvement in patient health outcomes and economic efficiency. The stakeholders across the patient treatment value chain – pharma, medical devices, providers, payers – have realized that patient centricity and focus on health outcomes improvement are the “north star” for the industry. The demonstration of development on these dimensions has become an essential requirement for significant decisions regarding the patient’s health. The industry had made significant investments in generating health or real-world data across patients’ health journey to make rapid decisions, across various healthcare decision points.

Innovative, plus traditional, Real World Data for comprehensive patient insights

For the Life Sciences industry (pharmaceuticals, medical devices, clinical research organizations, biotech, digital therapeutics, and others) insights and evidence generation with a focus on leveraging the real-world data are becoming instrumental in drug development and commercialization process. The rapidly increasing patient-centric data is informing the process and decisions from discovery, clinical development, trial design, trial outcomes (e.g. efficacy, toxicity) to identifying best treatment setting, provider education, value effectiveness for re-imbursement and overall long term impact on patient health-life.

Today, Life Sciences industry has access to multiple patient centric data sources including claims data, EMR, Lab/diagnostics, patient registries, pharmacies, disease registries as well as innovative, emerging digital health data including wearables, digital implants, health app, social media and much more. The availability of these digital and innovative data sources has significantly increased the ability to have more comprehensive insights into patient health and effectiveness of the prescribed treatments.

For example, drug development is an extremely expensive process and often relies on an inefficient clinical trial process. Over the last few years, RCT data is being married with claim administrative data to deliver insights into the effectiveness of treatments. However, claims data will show that a patient filled a prescription but wouldn’t necessarily give insights into health outcomes, improvement in diagnostic parameter or side-effects. With various sources and abundance of patient-centric data, such gaps regarding patient health insights can be easily bridged, providing life sciences organizations the relevant solutions to advance the drug development process.

From Real World Data to Real World Patient Insights – The challenges

Having access to real world data is merely the start of patient insights journey. Analyzing these rich sources of patient data is critical to uncover numerous deep insights such as which practices are most effective in improving overall patient health life or which approaches, and interventions can significantly improve the effectiveness of patient care. With this view, several Life Sciences organizations have become increasingly aware of the importance of the application of cutting-edge data science and analytics techniques to derive meaningful insights from the complicated and vast real-world data sources, which is why life sciences organizations have started on their journey to analyze real-world data.

While this is a widely accepted thought process today across various stakeholders in the healthcare value chain, however, the execution capability to leverage advanced data science and machine learning is still a constraint. With the complexity involved in the patient data universe, analysts end up spending a significant amount of their effort and time in underlying data preparation and integration activities. These challenges leave behind an unrealized potential in deriving outcomes and insights from multi-dimensional patient data from various sources.

Data Science Platform to accelerate Real World Insight for faster action

With the growing demand for patient insights and evidence generation, a bespoke analytics or study execution approach i.e. grounds-up execution for each study, is an extremely inefficient approach and will somewhat hinder the speed and agility required in decision making in today’s health world.

Consequently, a Real World Evidence Platform driven approach which can deliver impact and value across RWD Studies and delivering patient insights using Data Science at scale is critical to enable real-world insights as a core enterprise asset.

Focused on bringing the best-in-class AI/Machine Learning capabilities to Real World insights, the Real World Evidence Platform also plays an assistive role in enriching the productivity of analysts in multiple ways.

Advanced Analytics (AI/ML) for Real-World Insights

Modern life sciences organizations can possibly leverage AI/ML, predictive analytics capabilities to enhance the insights generated from real-world data. With the ability to uncover insights from new data sources, formats (text, unstructured, wearables) leveraging Natural Language Processing and Natural Language Generation, the platform provides relevant solutions for improving public health.

Pre-built Machine Learning Model Libraries

Pre-built model libraries can integrate new user-defined models with the flexibility of external tool integration (e.g., Excel, BI/reporting systems), providing healthcare players with promising health outcomes and higher productivity.

Reduced scale-up time for new studies

The platform uses standardized analytics processes, which dramatically reduces the ramp-up time along with evidence synthesis templates for a rapid generation of insights resulting in an extensive array of desired results. Also, the use of automated ML model workflow and performance monitoring can possibly revolutionize how life sciences organizations can work to reduce spending and improve patient outcomes.

Improved Reusability

The reusable data features and models help build institutional knowledge for the healthcare players. Moreover, the pre-configured patient data model covering patient 360 profile, diagnosis, procedures, physician, Rx prescription, digital health data that is suitable for healthcare professionals assists in drawing critical actionable insights in the context of patient care. The platform also presents a flexible data model designed to integrate new, evolving patient data elements.

Deeper patient health insights generated at scale to drive better health outcomes – is a critical imperative for the Lifesciences industry as it aspires to play a leading role in the patient health continuum. There are certain limitations of current bespoke Real-world analytics approaches. Such restrictions are a serious deterrent in this pursuit and Life Sciences organizations’ ability to deliver on the healthcare “north star” – Improve Patient Health Outcomes.

Even in stable times, Pharma supply chains are fragile and as complex as they can get. As the Covid-19 pandemic continues to wreak havoc on countries around the world, Pharmaceutical supply chains have come under immense pressure. In this article, we would cover some of the key challenges which Pharma supply chain Executives face on the frontlines, and how Analytics and Data Science can be leveraged to overcome these challenges.

Covid-19 disruptions are going to test the strength of Pharma Supply Chains:

Stockouts are a real risk: According to a study by University of Minnesota, 80% of the drugs marketed in the United States, including 19 of the 20 top-selling brand names, are made overseas. The global nature of supply chains, regulatory challenges and high uncertainty makes stockouts a real risk – jeopardizing the health of millions of patients who depend on life saving drugs
Operational metrics for Pharma Cos would need drastic improvement: With an average inventory of 258 DoH , the Pharma industry has one of the largest inventory stockpiles – 2-4X larger than FMCG at 72 DoH. However, given the staggering scale of this pandemic and the real risk of stockouts, Pharmas would need to rethink and optimize their inventory allocation strategies to ensure that the current inventory of drugs is allocated to the channels and regions with the most urgent need
Shift in Consumption Patterns: As drugs are identified as potential treatments for Covid-19, the demand may quickly surpass the supply. These drastic shifts in consumption patterns have already been observed with Covid-19 treatment candidates e.g Genentech’s Actemra, Sanofi & Regeneron‘s Kevzara and Gilead’s Remdesivir, an experimental drug for Covid-19. In early April, the FDA reported shortages of hydroxychloroquine and chloroquine, antimalarial drugs that were speculated to be front-runners for a possible Covid-19 therapeutic. This shortage has impacted patients with Lupus, where chloroquine is a life saving drug
Long-term Cyclicity of a Recurring Pandemic: Taking a page from history, the Spanish Flu epidemic hit in waves, the second wave more lethal than the first. If the Covid-19 virus proves to be seasonal, the impact of the pandemic might happen in waves over a 1-3 year period before stabilizing. Pharma Cos should be prepared for detecting and responding to new drivers of demand with very high momentum. Some new drivers of demand could be – preference for self administered drugs due to a drop in hospital visits, higher propensity for hoarding and increased demand for normal uses of certain drugs e.g acetaminophen to treat fever & flu symptoms

Given the scale of disruption, how can Pharma Supply Chain Executives approach these challenges ?

A combination of strategic and operational moves leveraging Analytics and Data Science capabilities will help you get to the critical insights necessary for getting started.

Gain a realistic view of your current state: Creating a transparent view of your supply chain and assessing the current state is your first step. Quick dashboards and ad-hoc analysis will give you a perspective of what is happening on the ground, and in the moment. The views should be built to assess 3 key stages in your Supply chain:
- Multi-tier Supply Assessment– What are the most critical components of Supply? What is the risk of interruption? What is the next best action for high risk Suppliers?
- Inventory Audit– Where does your allocated Inventory lie both in-house and with distributors? What amount of this Inventory is finished goods vs blocked for quality control and testing? What is the volume of Inventory in transit?
- Demand – What is the most realistic estimate of customer demand? Are there any specific NDCs with disproportionate impact on demand? How is the demand distributed at the Distributor, Geo and NDC level? Are the underlying assumptions of demand signals still robust? What are the emerging drivers of demand which might be getting missed?
Break down your perspective – Short term and Medium term: Organize your efforts with a Covid-19 Command center which would provide a perspective on ‘Short term Crisis management’ and ‘Medium term Planning Ahead’ initiatives. This would ensure that teams on the ground continue to have bias for action, without getting blindsided by what’s coming ahead.
- Crisis Management teams– Focus on the most immediate tasks where speed is of essence. This team would focus on the most high impact disruptions and build quick dashboards/reports to get a transparent view of the current situation and generate critical insights for operational teams on the field
- Planning Ahead teams– Look ahead to answer questions on mid-term and long term impact – like testing underlying assumptions, bringing in new intelligence from external analysis, identifying and integrating new signals and data sources into the analysis and developing scenarios for the future
Develop Scenarios for multiple versions of the future: The nature of the current Covid-19 pandemic is such that the arc of impact would be varied and staggered across the world. Take the US for example, every state and county is experiencing the pandemic differently. Hence, supply chain teams need to develop a scenario based decision making frame to assess how the pandemic would pan out, and what are their best moves at the moment . Here , the scenarios need to be built at 2 levels :
- External Scenarios– Evaluate impact of the pandemic and effectiveness of the response at macro and micro levels across countries, states and down to county levels on key metrics like demand and supply
- Internal Scenarios – Simulate impact of moves based on a Pharma’s ability to respond to the crisis. This would involve tweaking Supply chain parameters like manufacturing and shipping lead times, safety stock assumptions to identify what is the next best action that should be taken by Channel Inventory, Demand Planning and Manufacturing teams in the medium and long term
Factor for Uncertainty and Anomalies: During times of uncertainty , one of the most powerful tools in the arsenal of Data Science is Anomaly detection. Over here, the unknowns are shifts in consumption patterns and cyclicity of recurring pandemic which would be hard to detect with human judgement. Once fed with the historic data, powerful ML algorithms can help you quickly spot unknown unknowns in your data, down to the most granular levels of detail – helping you set up algorithmic trigger points to flag alerts. This should be one of the key pillars of response for the ‘Medium term Planning Ahead’ workstream. Some examples of metrics to be looked at:
- Analyzing past demand patterns with anomaly detection models would help quickly spot which NDCs are impacted by the shifts in consumption patterns to predict stock outs at zip code level
- Anomaly detection algorithms to flag emerging new hotspots of emerging Covid-19 cases at a country, state and county level impacting distribution and logistics
Be prepared for a fundamental change in the nature of your Data: With more than 3 billion people in lockdown, this epidemic will bring dramatic changes in patient, distributor and regulatory behaviors around the world. Covid-19 is a perfect example of a ‘structural break’ in your time series data – with implications both in the short and the long term. Pharma companies might need to look for unconventional sources of data for getting insights during times of uncertainty. For example – Google Search trends have been found to be good predictors of demand for certain types of drugs, and can also help us find emerging Covid-19 outbreaks. They can also reveal symptoms like ‘loss of smell’ that at first went undetected.

The current crisis has plunged entire countries and the Pharma industry into times of uncertainty. Building a transparent view of the current state, scenario based planning and proactive detection of anomalies are key tools on the frontlines for defense.

By acting intentionally today and using the tools at our disposal, Pharma companies can weather this crisis, emerging stronger and building resilience for the future. And in the process, enhancing and saving many lives around the world.

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

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!

Patient Marketing: Changing Landscape

I have seen patient marketing in HealthCare undergo significant change in a rapidly transforming commercial and digital environment. There are numerous factors disrupting the status quo:

Increasing role of patients in treatment and care
Competition for “high-value” patients for “elective” provider service lines (e.g. Knee Replacement)
The availability of large “volume” and “variety” of data, and tools to organize and mine it
Growing importance of alternate channels (increasingly digital) to reach prospective patients
A steady increase in HealthCare digital marketing spend; albeit it’s still significantly lower compared to other industries

Personalization: From Consumer Internet to Patient Acquisition Marketing

Traditional patient marketing by HealthCare providers presents a number of challenges:

It is rules driven broad based targeting and does not mine available data to create a patient 360 profile for personalized outreach
The importance of patient journey map and the role of “influencers” is not explicitly mapped
Channel preference is not explicitly modelled or accounted for
Past campaigns and channel outreach not incorporated

Given the changes in the commercial marketing environment, it is imperative that HealthCare Providers drive personalization in patient targeting – the “right patients” to target with the “right channel outreach” and the “right message”

The native digital firms are at the cutting edge of using advanced AI/ML modelling techniques to drive effective consumer targeting. National retailers’ online loyalty programs are a good example of how these firms leverage data to optimize targeting. They use multi-year history of data (website visits, clicks and conversions along with email opens, clicks and subsequent conversions) in Bayesian machine learning models, to create a propensity score for each product-consumer segment. This is employed for personalized e-marketing and user experience, with estimated marketing ROI improvement of around 20 percentage points.

Some key learnings from these firms include:

Untargeted outreach is ineffective – users tune them out
Machine learning models can leverage history of user behaviour on emails, apps, website and offline data such as phone calls or sales contacts
The models can be operationalized to power personalized experience on all “user touch points”, such as emails, website, call centre etc.
Personalized experiences leads to user delight and increased response rates, as well as reduce churn and improve ROI of marketing channel promotions.

Bringing Learnings and Best Practices from Digital Native Firms to HealthCare

In order to leveraging the experience from digital native firms, it is imperative that we bring the “Best Practices and Learnings” to the HealthCare business context:

Patient Acquisition Marketing Personalization: Problem-Solving Framework

A structured problem solving approach is necessary to analyse personalization in patient marketing. A key part of this framework is to link the

propensity prediction and recommendation to patient segmentation and available data (patients with some history within our system vs. new prospects).

In addition, the prioritization must be linked to core business objectives e.g. drive business in “orthopaedic surgery line of service as part of overall expansion and profitability strategy”

A broad outline of the problem solving framework and its constituent parts is shown in the illustration below:

In my experience, the key to driving personalization in patient acquisition is:

Creating a 360 patient profile including the journey map for existing patients
Choice of “appropriate” ML modelling approach based on patient segment (existing vs. new prospect)
Channel preference modelling based on available data and/or experimentation approach to try out alternative outreach strategies
Ability to augment modelling results with business priority driven rules
Integration of existing patient outreach infrastructure to deliver marketing message via the appropriate channel

Program Execution

There is great diversity across organizations. In order to successfully execute personalized targeting and digital marketing solutions, it is also imperative to define the right operating model that aligns with business objectives, organizational strengths, and the right partner(s).

Here is a brief outline of an execution approach that I have been seen work well:

The output from the “personalization” analysis is executed thru the existing patient engagement platforms
It is also imperative to deploy an A/B testing framework to improve our channel outreach and messaging over time
Finally, a “system of insights” that can track the effectiveness of our marketing efforts and deliver both “insights” and “course-correction” recommendations is key for ongoing success and to optimize program ROI

In conclusion, a data-driven approach that leverages the best in class AI/ML modelling; implemented within the context of our organizational engagement model, and focused on key business metrics is critical to driving successful implementation of patient marketing programs.

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.

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