Tag: decisioning

Several months into the global pandemic and we know that general indicators of risk or stress don’t reveal enough about what’s really going on within your customer portfolios. We also know that most institutions heavily use statistical models in identifying and capturing risk drivers in order to make decisions. Active model calibration in current circumstances can have a measurable effect on approvals and expected loss within a few weeks of being implemented. Banks have managed through economic recessions and other stressed scenarios by adjusting various levers for liquidity and risk. None, however, have ever had to predict consumer behavior in a pandemic. How can credit risk executives regain control over disrupted risk models at a time of constant change? Four key actions to enact now for immediate and sustainable impact:  1. Increase the frequency of model health monitoring Many of the predictive models that financial institutions rely on aren’t stable enough to handle real-world disruptions. Nor are the models re-calibrated frequently enough to appropriately assess risk in the rapidly changing situation we currently face. Monitoring models on a quarterly basis isn’t enough, but that tends to be the average frequency for most financial institutions. Increasing the frequency of model monitoring processes and identifying the need for a change in models sooner leads to significant financial impact. Depending on the asset size of the institution and the specific use case, financial institutions can potentially save millions of dollars in lost revenue or avoided credit losses. Automating the process supports an increased frequency of monitoring while requiring less effort from your analytics team. 2. Carry out ex-ante stress testing for your models Businesses should consider using ex-ante stress testing, in light of the difficulty in maintaining the accuracy of model predictions in changing conditions as well as to meet the heavy governance requirements of new models before their actual use. Traditional ex-post processes are effective in simulating what would have happened historically had a new model been in place. This is an extremely valuable exercise but isn’t very helpful in the current stress environment which is both unique and highly uncertain. Risk managers would like to have a go-forward view on model performance for decisions being made right now, not just a look-back view on decisions made historically. Applying ex-ante stress testing allows us to simulate and analyze a range of possible outcomes based on changing macro conditions, evolving consumer behaviors, and other uncertainties like the quality of underlying data. 3. Make practical, short-term adjustments We’ve seen in previous economic downturns that models can rapidly become unfit for purpose, and the consequences may not be fully apparent until long after the start of the downturn. In such circumstances, you shouldn’t necessarily attempt to make changes that you expect to be robust for many months into the future. There’s a strong case for making adjustments that are designed to address temporary circumstances and reviewing them at an increased frequency. Some businesses are taking a conservative strategy by tightening their credit policies and decisioning strategies. Other businesses are overlaying their models with certain attributes. For example, one could look at the number of open inquiries in the past 30 days. Since we know that attribute is unstable, we can pair it with an attribute that will give you more population stability – such as average open inquiries over the past 6 months. 4. Setup for rapid re-calibration or re-build of models The decision to re-calibrate or re-build a model during the pandemic would depend on multiple factors including the business need and model use case, the performance of the existing model, and the confidence in the quality and relevance of data for the model build. However, it is important that financial institutions and other businesses are set up to rapidly update their models. They should be actively working on re-calibrating/re-building their models in a test environment, evaluate the impact, and be prepared to deploy.  The ability to rapidly update models will be a key differentiator as businesses compete to grow their portfolios and manage losses during and in the aftermath of this pandemic. As with many other aspects of our lives, credit risk management is being challenged by the new reality created by a global pandemic. Whether our response is temporary, or whether the crisis is accelerating an existing trend to be more active in model management, we need to react to maximize our portfolio performance. At the end of the day, none of us have been through a pandemic but we know our models can still work. It’s all about model accuracy and model governance and reducing error rates. By increasing the frequency and efficiency of model monitoring and re-calibration, we can drive business outcomes with more impact than ever before. Learn more: For many organizations, navigating and recovering from these volatile times will remain top priorities as they begin strategizing for the future. Get details on accelerating your digital transformation.

Whether you work for a small or big company, chances are you’ve seen budgets contract in the wake of Covid-19.  There are a lot of factors contributing to it: fluctuating economic outlooks, building up loan loss reserves, and re-directing expenditures to keep employees and customers safe and secure. A recent global study of banks and retailers found that the top area of short-term investment was securing the mobile and digital channels. In fact, it also showed that 80% of businesses put a digital identity strategy in place, a 30-point increase since Covid-19 began and 60% of businesses are planning to increase their budgets for credit risk analytics and fraud prevention, respectively. So why is it that only 32% of banks and retailers feel operationally ready for their customer’s continued demand for digital engagement? The Capex required to invest in new technology these days requires a fiercely competitive business case. Not forgetting to mention, if approved, it could be a while before you see a return on your investment. But it doesn’t mean the latest advancements and innovation available for managing credit risk or fraud risk is out of reach. Getting more out of your existing tools and technologies is easier to implement and quick to deliver results. In fact, since Covid-19 began, hundreds of clients have optimized their use of credit and fraud risk software and analytics, helping them focus on creating more meaningful customer relationships and saving them millions in potential losses. Here are two examples of how you can get the most out of your existing technologies today and a checklist for evaluating your current tools. Device recognition Beyond securing systems against Cybersecurity threats, businesses need to think like the criminals they’re trying to deflect. If it seems like the world all went digital overnight because of Covid-19, then you can bet fraudsters were one step ahead exploiting the blind spots in the customer relationships you quickly moved online. But how do you recognize your customer behind their mobile device or computer screen? One way is to discern a fraudulent (or “mimic”) device from a genuine one. Having access to this information allows you to swiftly see the same device repeating both good and bad behavior and thus have a better chance of isolating the mimic device and mitigating fraud attacks. This is done by creating a strong probabilistic measure to determine whether two events are from the same device or not. How does this help? It helps to reduce over-firing fraud velocity rules and more precisely out-sort fraud events for manual review. It’s not as complicated as it sounds, and many businesses already have access to this device intelligence data which simply requires them to either turn it on or upgrade their fraud management systems to its latest version. In fact, additional device data points are always being added, and upgrading this layer is often recommended as it can provide up to 85% improvement in performance. Bottom-line: Device data bolster the effectiveness of your customer identity and fraud defenses with little impact on operational resources and reduces friction on your customer’s digital experience. Machine learning Innovations in decision management are having an impact on areas traditionally associated with predicting consumer behavior, such as credit risk, collections, and fraud detection. The ubiquity of data nowadays requires the methods used to derive actionable insights to evolve and most lenders globally have started to adopt advanced analytics. Nearly 70% of businesses increasing their use of machine learning for determining creditworthiness since Covid-19 began. For the collections process, it has helped to determine the best way to contact a delinquent customer or the best treatment to use as a customer exits Covid-induced forbearance?  For card, mortgage, and automotive portfolios, machine learning has played a strategic role in creating and implementing pricing strategies to determine the most accurate decisions for financing terms. Perhaps it’s in fraud detection where machine learning is having the biggest impact. Unlike how it’s applied in credit risk decision strategies, machine learning used for fraud detection can be trained to learn and improve with experience without explicitly being told to do so. It excels at solving problems where the “problem space” cannot be defined easily by rules, which makes it a great complement to mature rules-based fraud management systems. Furthermore, machine learning models can take advantage of the different data points from all backing applications at the time of any single transaction, login, or submission. This produces a final decision that’s more accurate than that produced by a simple rules-based approach or manual decision matrix. Attributes that once provided minimal lift when analyzed in a silo may now provide a substantial lift to predict credit risk or prevent a fraud attack when combined with multiple data elements. Conversely, legitimate events that were inadvertently triggered by traditional fraud detection methods can be identified as authentic before having a negative impact on the customer’s experience. Bottom-line: A layered approach continues to be a key component in any credit decision or fraud detection solution and machine-learning models are the final call in your decision workflow strategy so they can leverage all the previous decision data. Checklist: Evaluate whether you’re getting the most from your decision technology Is your current solution providing the results you need? Avoid comfort in patterns and request a business review of your current solution to analyze performance. It may reveal unknown gaps and opportunities to improve your business results. How do your results compare to your peers? Some peer benchmarking is publicly available, but most vendors offer peer (blind) benchmarking using your specific performance data. It’s worth the ask! Are you using all the functionality your tool has to offer? Sometimes decision technology is implemented with a myopic focus on solving a specific problem or used in a specific area despite a broad range of functionality available that covers more use cases. Are you using the most up-to-date version of your tools? Check with your vendor right away and stay informed regarding newer versions. Upgrades generally require less effort and cost than a new solution and by continuously monitoring for the latest version, you’re able to meet current regulatory and policy standards. Are there any ‘add-ons’ available? Your existing decision technology may offer add-ons to enhance your current solution. Add-ons such as new or enriched data sets, updated scores or models or new software features may extend the business usage of a solution to different processes and within additional departments. Are your technologies integrated to enhance your credit risk and fraud risk decision workflow? Integrating your technologies can help you to execute credit and fraud strategies seamlessly with less chance for error, manual intervention, or duplicating actions across disparate systems. Technology is critical in meeting customer demand and staying competitive in any market. It can help balance the demand for internal resources while providing the service your customers deserve. But as organizations look to stay competitive, and agile through a volatile economic time, remember the importance and tangible benefits of optimizing what you already have in place. Related articles: Global research study: The impact of Covid-19 on consumer behaviors and business strategies Podcast: Banking trends and opportunities in the post-Covid-19 era Are traditional online identification methods becoming obsolete? Case study: Layered behavioral biometrics, device intelligence and machine learning 

The global pandemic led to swift and unexpected shifts in consumer behavior, from the significant increase in the use of digital channels, to the decrease in ability to pay for many. Based on this environment, we’ll highlight where senior financial services executives should focus their analytics and decisioning teams’ efforts to provide a bit of certainty in an uncertain time: Confidence and demand for credit First off, it’s important that lenders consider current dynamics when monitoring and measuring the effect of fluctuating market conditions on their portfolio. Overall lower consumer confidence in the ability to access credit is not surprising, but the true impact on demand for credit is yet to be concluded. “As a result of both the pandemic itself and the changed economic conditions it caused, consumers’ appetite for new credit and the ways in which they are using existing credit are in flux.” – Leslie Parrish, “Uncertainty Is Certain: Consumers’ Financial Outlook at Mid-Year 2020,” Aite Group, July 2020 From late June to early July 2020, we surveyed 3,000 consumers and 900 businesses in 10 countries. This research indicates some consumers are responding to economic uncertainty by reducing spend and tapping into financial reserves, while other consumers are using credit to make strategic decisions such as refinancing, buying a new house, or opening new lines of credit for access to money. Regardless of customer sentiment, it's important for businesses to understand these realities: Consumer demand for digital is increasing — our research shows it's gone up 20% since Covid-19 Digital channels will help fuel new business — with a marked 40% increase in consumers opening new loans digitally based on our research These indicators should drive investment in solutions to secure the digital channel and improve digital onboarding, including data, analytics, and technology. Such investments help meet consumers’ digital demands, safeguarding your ability to retain existing customers and win new business. >> Download the Global Insights Report Ability to pay Lenders should also be mindful of the volatility of the current environment and ensure their teams rely on data and analytics that enable accurate decisions based on a consumer’s current financial situation. Given active programs established to supplement a decline in consumer income, we are still enjoying a nourished economic environment. However, our research shows that globally, since Covid-19 began, the number of consumers having difficulty paying their bills has doubled, and according to Aite Group, half of consumers in the U.S. reported their household has suffered a loss of employment income since mid-March.1 These conditions enforce the need to have the right tools in place to best assess consumer creditworthiness. Decisioning in the new norm As lenders continue to focus on business health, it’s key to consider operational efficiency and ongoing optimization. Given there is no precedent to the current global pandemic, lenders will need to rely on innovative solutions to learn and adapt in real-time. Our research shows that many businesses know change is needed and are seeking solutions to tackling this challenge. One in five businesses globally lack confidence in the effectiveness of their credit risk and collection decisions since Covid-19 began. Sixty percent plan to increase budget for analytics and credit risk management. Meanwhile, the top three solutions businesses believe will improve operational efficiency when supporting customers’ financial needs are: automated decision management, cloud-based applications, and artificial intelligence. To keep pace and be successful through this unchartered territory, lenders must leverage innovative technologies such as cloud-enabled solutions, artificial intelligence, and machine learning. Though today’s lending environment is likely to include levels of volatility for some time, making the right adjustments now can help lenders support consumers and business performance in the long term. >> Get more insights on the impact of Covid-19 on consumer behaviors and business strategies _____ 1 “Uncertainty Is Certain: Consumers’ Financial Outlook at Mid-Year 2020,” Aite Group, July 2020

Due to Covid-19 , the focus on analytics and artificial intelligence (AI) has significantly increased. However, while companies have made significant investments in AI, many are struggling to show a tangible impact in return. One executive commented, “We have data science teams and a data lab where advance techniques like neural networks, GANs, etc. are successfully being used. However, less than 10% of our actual operational decisions and products are powered by AI and machine learning (ML). I would like us to be driving greater measurable impact and Covid-19 is exposing some of our execution gaps.” And, he’s not alone. Despite the investment, the true impact is elusive, and many businesses are not getting the desired effect from their efforts. Achieving the results needed to justify continuous investment will take a holistic approach. So, what can companies do to achieve this impact? The four pillars of holistic AI: performance, scaling, adoption and trust Achieving impact from AI requires taking a more holistic approach across four pillars — beyond just the delight of the data scientist producing a better performing model. 1. AI performance — outperforming the status quo and quantifying the impact This pillar is where most data scientists and companies tend to focus first, for example using modern AI techniques to create an underwriting model that performs better than traditional models. The so-called ‘data science moment of truth,’ where the data scientist declares that he has built a model which outperforms the status quo by 10%. However, it’s important to note model performance alone is not sufficient. We should look beyond the model to understand business performance. What quantifiable business impact does the 10% improvement deliver? How many more credit approvals? How much lower will the charge-off rate be? This reasoning provides the important business context around what the incremental performance means. 2. AI scaling — having the right technical infrastructure to operate models at scale This area is often ignored. The risk with data science teams is they can see their job as being completed with creating a better performing model. However, that’s just the beginning. The next important step is to operationally deploy the model and setup the operational infrastructure around it to make decisions at scale. If it is an underwriting model, is it deployed in the right decisioning systems? Does it have the right business rules around it? Will it be sufficiently responsive for real-time decision making, or will users have to wait? Will there be alerts and monitoring to ensure that the model doesn’t degrade? Are there clearly defined, transparent and explainable business strategies, and technology infrastructure and governance to ensure all stakeholders are aware? Is the regulatory governance around this model in place? Does the complexity in the model allow it to scale? Too often we see data scientists and data labs create great models that can’t scale and are impractical in an operating environment. One banking executive shared how her team had developed 5 machine learning models with better performance, but were in ‘cold storage’ verse in use, because they didn’t have the ability to scale and operationally deploy them effectively. 3. AI adoption — ensuring you have the right decisioning framework to help translate business decisions to business impact With better performing predictive models and the right technology, we now need to present the information in a way that is ‘human-consumable’ and ‘human-friendly.’ At one bank, we found they built a customer churn ML model for their front lines, but no one was using it. Why? They didn’t have the contextual information needed to talk to the customer — and the sales force didn’t have faith in it — so didn’t adopt it. Subsequently, they built a model with a simpler methodology and more information available at their fingertips — where decisions could be made. This was immediately adopted. This pillar is where the importance of decisioning tools is highlighted. The workflow and contextual information to allow a decision to be orchestrated and made is critical in driving AI adoption. 4. AI trust – having governance, guardrails and the appropriate explainability mechanisms in place to ensure models are compliant, fair and unbiased This final pillar is probably the most important for the future of AI — getting humans to trust it. In recent times we have seen numerous examples like the Apple Card, where the underlying principles and models have been called into question. For scalable AI impact, we need an entire ecosystem of people who can trust AI. To achieve this effect, you need to consistently apply the right principles over time. You also need the right decisions to be explained — like adverse action calls. Explainability capabilities help manage communication and understanding of advanced analytics, contributing to established AI trust. And, when fairness and bias issues come up, you need to provide good answers as to why decisions were made. AI is poised to fundamentally change the way we do business, and studies show that $3 to 5 trillion in global value annually, up to $15 trillion by 2030, is likely to be created. We believe the four pillars highlighted above will be key to accelerating the journey to driving positive results and capturing this value. At Experian, we are making investments to drive impact for our clients by delivering against these four pillars. Related articles: What is the right approach to AI and analytics for your business? Four fundamental considerationsHow rapidly changing environments are accelerating the Need for AI What’s new in online payment fraud Part 2: How AI and evolving regulation are driving change

In this episode of the Insights in Action podcast we talk to Neil Stephenson, Vice President of Strategic Client Development, about how businesses can address a lack of data. Following an earlier episode tackling business data challenges, we discuss getting value from the data your organization already has access to, tackling legacy software issues, the accelerated shift to customer-centric technology stacks, and an increase in industry partnerships to solve common challenges. Nearly a third of senior business leaders say they don't have enough data to get insights they need, or that the quality of the data they have access to is poor. We take a look at the three steps businesses need to take to address this challenge, starting with the quality of data already in the business. "We see a number of organizations that have pretty powerful data within their own business but don't leverage it as well as they could, so matching data together and making sure they've got a really strong view of their customer across all of their systems is really essential, and then having processes ongoing to make sure that they maintain that view whenever they touch the customer, whether that be through an online channel or face to face." Neil Stephenson, VP, Strategic Client Development Listen to the full episode here, and look back at the previous in the series, Solving key business data challenges - with Bill O'Connell, Experian Global Decision Analytics

Recently we commissioned Forrester Research to look into senior executives’ perceptions on key business data challenges and the importance of achieving a holistic view of their customers. This research uncovered that nearly a third of business leaders worldwide say they don't have enough data to get the insights they need or that the quality of the data they have access to is poor. While getting the type, quality, and amount of data right is paramount to success in your endeavors to create actionable insights that take your business to the next level, data alone is not enough. To get value from data, there's a whole ecosystem that needs to be in place that enables the business to create, manage and maintain a holistic view of the customer, create analytically driven insights into those customers, and deploy them into production environments that drive optimal customer actions and journeys. Organizations also have the opportunity to explore new data assets from traditional sources or those dynamically created in a myriad of places across mobile devices and the Internet of Things. There must be systems and procedures in place to continuously improve and assess these new data sources, by bringing them into analytical processes where insights are derived and predictive models generated.  The critical task is then to seamlessly ingest and embed the data and models into production environments in a robust and compliant way. And that's got to be a continuous process. Otherwise, businesses will stagnate, and they will lose out to those competitors who are actively doing this. Addressing the lack of data your business needs to get actionable insights: Three practical steps Prior to even considering external or additional data sources, you need to get a solid understanding of the data you currently have access to within your organization, what value those data sets bring in and what are the gaps to be filled.   You should also review your internal processes and technology stack to understand if further IT investment is required to create a more effective ecosystem.  With the right tools and processes, you must be able to easily assess the uplift of new data sources in your analytics environment, as well as ingest those new data sources into production environments, to drive new models, run segmentation rules, and execute customer-centric actions. What are the three steps you need to take to get enough data to gain business insight you can take action on? Look at the quality of your internal data. We see a number of organizations that have powerful data in their own business but don't leverage it as well as they could. So matching data together, making sure that they've got a really, really strong view of that customer across all of their systems is really essential. And then having processes ongoing to make sure that they maintain that view whenever they touch the customer, whether it's through an online channel or face-to-face, so that they always know who that customer is, and they can match them to their existing relationship profile. Getting your internal data process correct is a foundational element to this whole piece. Understanding the value and role of new data. In terms of new data, it’s about understanding if that new data can actually add value to the business rather than plugging it into core systems straight away.  You need to work with the vendor or the source of that data to get hold of a dataset, match it to your customers, and run analytical processes to identify whether the data adds value. If it does, consider what models or segmentations could you create from that data that'll actually drive value in the business.Identify the software and architectures you have in place that allow you to connect to data and drive that data into a tool that can dynamically apply models and rules in a heavily regulated environment.  With the right toolset forming the bridge between your off-line analytics environment and your on-line production environment, you can leverage predictive data to continuously improve your customer-centric decisoning across the lifecycle for all of your portfolios.

The decisioning landscape is changing rapidly. In parallel to this, digital continues to redefine the customer experience with a big focus on removing friction from the customer journey. Mounting expectations around online customer experience mean that we are seeing a digital transformation both in terms of consumer interaction, and what the businesses are processing in the background. The front and back end are no longer mutually exclusive, and the driving force behind this transformation is digital, and it’s enabled by the cloud. How the pandemic has shifted priorities Before the Covid-19 pandemic took hold, businesses were well on their way to recognizing this. Digitizing more workflows while incorporating a truly customer-centric view was the goal of 2020. A Gartner report shows that in January, priorities for CIOs centered around Cloud and DevOps. This push to shorten the development lifecycle by combining software development and IT operations into a single discipline, alongside demand for Robotic Process Automation, using bots to focus on automating high volume repetitive tasks, were top of the list for businesses. By April, these priorities had changed. Businesses quickly shifted their focus to the pandemic, and with that, the need to enable remote or home working. But Cloud remains firmly within the top three. We look at why cloud-first decisioning remains critical to digital transformation, now more than ever. Why Cloud-first is even more important now Managing cash flow: When a CIO is in the cost optimization mode and trying to conserve cash, scaling back on the use of existing Cloud technology can afford immediate cost savings. Cloud cost for infrastructure of the service, or platform of the service, and even some software of the service is often tied to the business. The less usage, the more savings. When a CIO needs to implement new technologies in 2020, Cloud can offer the most cash flow optimized needs to do so. Less cash is spent upfront to acquire Cloud technology than to buy data center systems or licensed software. Business agility: Cloud technology makes it much easier to keep systems up to date and secure, alongside feature enhancements and new releases. The Cloud minimizes lengthy and costly delivery projects with solutions that can be deployed in weeks, not months and years. Customer journey: Many established market leaders are running digital transformation programs that re-orientate their business away from functional and product silos to focus on customer journeys enabled by Cloud services. Keeping it simple: Simplification is crucial. Simplifying the IT environment with Cloud services that eliminate the need to manage hardware and other infrastructure. Using Cloud-native architecture to support auto-scaling, zero downtime for upgrade. Security is paramount: The challenge to identify and fight fraud by analyzing behavior during the data capture process is ever-present. Software needs to evolve all the time to adapt to threats, and it needs to continuously update with new features to help businesses remain competitive. Businesses need to protect consumer digital accounts from Account Takeover threats while balancing consumer convenience. Cloud-first impacts all layers, from consumer interactions to data sourcing and processing, from fraud detection to identity verification, and at the heart of areas like credit and decisioning. Integrated decisioning, and decisioning that is governed and can be clearly explained to both the auditor and to the regulator is the goal of every business, and it is enabled by the cloud.

There isn’t a roadmap for navigating through times like these but the reality can’t be ignored. The effects of the pandemic will forever change how lending businesses operate and engage with customers long after the health crisis is over. Businesses and consumers have basically been pushed to engage with each other digitally en masse and there are practical challenges that banks and financial services are faced with today that need to be addressed. Some of these issues require short-term adjustments to manage things like increased volume of call center inquiries with a remote workforce. But other issues have put a spotlight on massive areas in need of modernization such as the management of liquidity and risk. Businesses need to think critically about how they will use technology and innovation to transform their credit risk and fraud operations to better serve customers across channels. Here are three cost-effective strategies that will connect you with your customers faster and in their greatest time of need – now and post-Covid. Respond to the change in a fair and consistent way. Regulatory bodies and credit risk policies are designed to prevent against unfair lending decisions. But when federal funding to provide stimulus and pressure for payment holidays take hold, it’s creating a lot of uncertainty for how to handle its impact on the portfolio. Strong operational decision management capabilities provide businesses a way to quickly test new strategies and deploy them. In fact, this isn’t all that new to large banks and financial institutions. But smaller banks have considered it “out-of-reach”, a perception that isn’t true nor acceptable at time when there are solutions available on the cloud. A huge benefit to moving your strategy management to the cloud is the ability to flex up or flex down your costs at time when balancing your cash flow and discretionary spend or technology investments is a top priority. Flexing up for increased customer demand to handle hardship or government-backed small business loans is going to be fundamental during this crisis, and where cloud-based strategy management will really pay off. A further benefit is that you remove the complexity of the IT infrastructure and get access to enhanced features whether it’s new data sources, models, or improvements to security. This is especially important as we all know, necessity is the mother of all innovation and there will be a need to get more from your current software without wanting to replace legacy systems. Models that drive decisioning still work. Despite the lack of historical precedent for the current scenario, data and analytics are very effective in this rapidly changing environment. For example, many people are facing financial hardship right now which means businesses need a way to efficiently receive and process applications that out-sort those in need of special servicing. Understanding who was headed into default prior to Covid-19 and who is experiencing short-term default because of this situational unemployment is key for delivering the right products and terms. In fact, if there is anything transferrable from the 2007/08 recession (which was entirely different from what the world is experiencing now), is that you need to use analytics to discern habits from new behaviors and ensure you don’t use vanilla treatments for both. Businesses will undoubtedly see their analytics teams overstretched during this period, so now is the time to reduce the manual load and invest in machine learning and AI. These advanced tools can offer the fastest and best results for getting the right analytical capabilities or models in place. For larger organizations, this will mean having the agility to rapidly update and deploy existing models, and for smaller ones, it will mean building this from the ground up. To help, our data scientists have recently identified over 140 consumer credit attributes that can offer some insights even in unprecedented times to: Identify financially stressed customers earlierPredict future payment behavior accuratelyRespond to profile changes faster Re-define the customer journey. Businesses should remove all unnecessary friction by inspecting the customer journey right down to every click and interaction. Why is this important? It remains to be seen exactly what customer behaviors and expectations will take hold but it’s likely to leave a lasting imprint. The contactless way consumers engage with businesses puts more and more pressure on how effectively they’re using data and customer insights to make their interaction relevant. Relevance in the form of – Do I recognize that this is my customer enrolling in or accessing their account(s) or is it suspicious?What do I know about this customer to proactively adjust or deliver a contextually appropriate offer or the terms they will accept?Are there signs of “mental drop-out” or abandonment that signal improvements to the experience are needed?How can I deliver the same experience across channels and simplify complex transactions, like enrollment?Do my customers feel secure and do they trust my business to protect their information? This is an opportunity for organizations to reflect upon how they do business, both in terms of how effectively they operate, but also in light of consumers changing expectations about the way that they want to engage with the wider community. Beyond the data, having an appropriate and empathetic response to customers who feel stuck can increase rapport, build loyalty, and open new possibilities to work together in the future. Related articles: Digitally managing your at-risk customers most impacted by Covid-19Proactively restructuring debt to help improve customer affordabilityPredicting customer payment behavior in a time of extreme uncertaintyStay connected to your customers in times of unexpected change

The speed at which the world is feeling the impact of Covid-19 is unparalleled. Because of this customer affordability has shifted into the unknown and businesses are trying to react quickly to assess customer risk in a brand-new context, albeit a temporary one. We look at the five key areas businesses should be considering when it comes to customer affordability. 1. Looking to insights The last financial crisis taught us that the first line of defense for many organizations, large and small, is to move straight into proactive debt restructuring to reduce the volume of customers who would otherwise fall immediately into debt collection. This crisis is no different, but identifying those in hardship, restructuring debt at speed, and in line with restricted policies are where businesses should be focusing to successfully tackle this. 2. Keeping regulators front of mind As a result of the last downturn, many financial regulators are placing a much higher weight of responsibility on lenders to make fair and transparent lending decisions when it comes to affordability. Not just when it comes to new lending, but also how they act and behave within collections. These rules are not going to be relaxed, so it’s important that businesses continue to prove that they remain compliant. 3. Predicting what’s to come Anticipating arrears before they happen, and at speed, is fundamental to managing the restructure of debt effectively. Especially where traditional data sources provide less predictive value. For businesses without advanced and automated debt restructure or collections-based program to begin with, this is an opportunity to develop something that will carry them through this time of crisis and beyond. 4. Harnessing analytics and AI Thinking predictively means getting the right analytical capabilities or models in place, ideally harnessing Machine Learning and AI to get the fastest and best results. For larger organizations, this will mean having the agility to rapidly update and deploy existing models, and for the less mature, it will mean building this from the ground up (but quickly). Businesses will undoubtedly see their analytics teams overstretched during this period, so now is the time to reduce the manual load and invest in these capabilities. 5. Automation for demand control Making sure customers can deal with organizations digitally will be critical to maintaining customer experience. It’s just as important to ensure that channels are integrated and automated in the backend. Businesses are looking to omni-channel digital solutions to help feed new demand through the funnel without having the added complication of a restricted workforce. It has never been more important to automate. More on Decision Analytics