All posts by Chris Fletcher, SVP Decision Management & Cloud Services

There's been lots of discussion about what a return to normal will look like as we transition out of the global pandemic—and much remains up the air. However, our recent consumer and business surveys paint a picture that merits the attention of financial service and credit companies. The big takeaway: The Covid-19 crisis has bifurcated consumers, created extremes on both sides. On the one hand, many individuals coming out of the pandemic have more cash than they had going in. The crisis didn't impact their income, and instead, they've spent the year spending less than they usually would due to work-from-home mandates and local lock-downs. Our consumer survey from January 2021 shows that financial challenges have eased for younger consumers and higher-income households. Yet, at the same time, there's also a contingent of consumers who continue to struggle. One in three of our survey respondents reported that they still have financial concerns and a similar percentage are worried about their employment. We anticipate that the demand for support, service, and credit will be high from each side. So how can companies respond to the heightened need for credit products while continuing to service consumers who may need support? This is where digital solutions make all the difference. By employing digital onboarding and decision automation tools, you can rapidly increase your capabilities while also improving the online customer experience for all. A return to spending The U.K. provides a glimpse of what a staggered return to normalcy may look like. When shops and restaurants re-opened for business in mid-April, lines of people streamed out the doors and flooded the streets. With the country's re-opening culminating in June, many consumers will be looking to resume spending on items and projects that they've neglected since the pandemic's start. For example, our survey data reveals that consumers are becoming less cautious with their finances in general. Fewer people report that they're cutting back on discretionary spending and there's a decline in consumers putting money toward emergency funds and drawing funds from savings accounts. These consumers may be gearing up to spend more. And companies that can anticipate their needs and meet them proactively will be positioned to win and keep their business. Solutions for pent-up demand Many businesses are already preparing for this new wave of demand. Consider that eight out of 10 businesses report that they're turning to cloud-based decisioning applications to improve the customer journey. In doing so, companies are giving themselves much-needed flexibility right when it's needed most. They can dial up their online capabilities based on demand and then dial down if it drops. At the same time, these automated solutions enable companies to deploy their staff to customers who do require personal attention. It's a divide-and-conquer model that keeps the customer at the center. In addition to utilizing the cloud, more than 40% of companies say they leverage AI to improve the customer experience. The AI component enables companies to provide personalized options for consumers and create customer journeys that are far more relevant. The timing for such personalization couldn't be better. In our research, a growing percentage of consumers indicate they're willing to share more personal data about themselves in exchange for improved experiences and added value. Building solutions that work—for everyone The pending volume creates a significant growth opportunity and highlights why digital solutions are a must. Companies that provide the best digital service to customers will garner their trust, loyalty, and even referrals. This yields more demand, increasing the need for scalable, cloud-based onboarding and decisioning even more. Amid this activity, you'll want to focus on getting the most from your digital tools. To do so, consider: Leveraging data for improved credit outcomes Evaluate your end-to-end customer journey, looking for ways to utilize data and increase personalization at every juncture. You'll improve the customer experience and provide more relevant offers. The right data also provides a holistic picture of customer credit risk and ensures you're not creating problems for the future. Utilizing low-code solutions so employees can dive in Digital onboarding and decision automation can be game-changing for the customer experience. But if it's hard for employees to use, then that effectiveness takes a hit. Look for solutions that your employees can use off the shelf. The ability to generate customizable reports and execute on ideas and strategies without involving IT at every turn is essential. Recognizing limitations and potential bias Evaluate your analytics models and look for areas of limitation or potential bias. You want to ensure that you're providing access to credit to all eligible customers and not inadvertently excluding specific demographics. Building capabilities that put you ahead of the market The pandemic provided many lessons—and the value of anticipating demand or potential problems was one of the most important. The crisis is waning, but the financial consequences will continue to reverberate, especially as various government aid programs come to an end. Focus on improving your analytics so that they can better describe what's happening now and predict pending changes in demand and shifts in your portfolio. By and large, consumers are moving forward after a challenging year. Prioritize your digital solutions to make sure you can meet their needs regardless of what the future holds. Stay in the know with our latest insights:

In a world drastically and constantly changing, industries and technologies are being transformed at a rate never before experienced. Recently, I had the opportunity to speak with Roy Schulte, Distinguished VP Analyst at Gartner, about trends in the evolving world of big data, advanced analytics, and decision intelligence — trends that are powered by advancements in cloud technology, machine learning, and real-time streaming. Below, I will share a summary of key highlights. The growth of decision management More businesses are implementing decision management. They want to make more automated decisions to accelerate outcomes while improving applicability. In tandem, decisions are becoming more complicated with regulators increasingly expecting decisions to be explainable and with audit trails built-in. Equally, the combination of machine learning and decision management has placed greater focus on the importance of avoiding bias. Bringing all of this together promises continuous decision improvement; updating models and strategies in days or even hours rather than weeks and months. Essential in responding to the rapidly changing environments, none more so than the impact of Covid-19. As businesses are implementing decision management, they are putting the new systems into the cloud. Based on a Gartner survey in mid-2020, 67% of respondents said their digital business platform will be cloud-native application architecture. It’s the primary criteria for the architecture of many of these new systems. Migrating to the cloud The reason decision management is going to the cloud is the same reason other areas of business are taking this step. Organizations are highly motivated not to run their own systems. There is no competitive advantage to doing so. They want to entrust it to others so they can focus on what the business does best. The migration will continue gradually to the cloud, with a current acceleration based on Covid-19. In a recent Gartner survey, 65% of respondents said the pandemic accelerated their plans and funding for doing digital business. Most models and strategies will be built in the cloud, and the actual runtime decisions will be distributed with some on the cloud, some on-premise in a data center, and some out in the edge in a mobile device. Real-time streaming In the past, traditional business information was done on static, snaps of data from the past. Today, much of this is going real-time, depending on the kind of decision that is being made. For example, when a customer is visiting a website, there are mere seconds to generate the next best offer. This is a real-time decision. However, some decisions do not require real-time, such as the strategic decision to acquire another company or not. That is why it’s important to align the decision speed and cadence with the actual business problem. If real-time data will be used, such as for an e-commerce situation, some of it must be streaming data. With the increase in factors taken into account when making decisions, you need data that is connected, contextual and continuous. The data must come for your entire ecosystem, not a single department, but across your company, business partners, your customers, or market data. Streaming examples for e-commerce might include location, what the person has been doing on the web recently (clickstreams), and records of contact with your business such as calls and emails. A real-time lookup is involved with inventory and external factors like credit rating through an API, and customer data will include historical and real-time. With these factors, real-time decisions for e-commerce will be more effective, with higher yield rates and lower fraud rates. Machine learning in model building and execution Machine learning (ML) is making predictions, not decisions. When a prediction is made and a score is provided, a rule must be applied to determine outcomes based on the score. If considering rules or analytics, the truth is that in most cases, both are needed. The goal of ML in decision management is to have applications that are easier to develop, faster to develop, and lead to more accurate outcomes. To achieve this objective, a process covering all stages of a decision cycle is needed — Observe: Getting connected, contextual, continuous intelligence. Governance is key at this step to know where data is coming from and that it will be used in authorized ways. ML models and strategies must avoid bias and alternative data sources and lending criteria should be considered to expand the business without incurring increased risk. Orient: The next step is putting the data in context. When dealing with models at scale, it’s important to be able to track outcomes through tools such as performance dashboards. Eventually, this will lead to the hyper-personalization of models. Decide: Once models are built, strategy, rule authoring, and approvals are needed. Workflow and collaboration mechanisms help manage the process and accelerate the pace of developing new decisions. Act: Next comes the deployment of models. Using logic to make decisions across multiple applications accelerates deployment, often referred to as centralized management or reuse of decision factors. Feedback: Finally, continuous logging of decisions and effects of decisions. Tracking provides the ability to audit past decisions, explain what was done, and accurately post hoc remediate. Ongoing feedback also enables continuous decision improvement at an accelerated pace. The future of decision management includes decision intelligence In summary, there are five considerations for the future of decision management — A systematic approach to decision making, including a lot more automation and decision intelligence, is clearly on its way. Migration to the cloud is well underway with acceleration thanks to Covid-19. Equilibrium will be reached where some decisions are made at run time at other locations, but most of the development of decision-making, modeling, and strategies will be based on cloud platforms. Data science ML vendors have not focused on decisions. Some may come to realize the reason you do analytics is decisions and broaden the scope of what they do, or they may stay focused and instead partner with other vendors to enable end-to-end decision making. For certain kinds of logic, graph databases and graph analytics can be very powerful. Likely this will become a big part of decision intelligence going forward. Finally, there is huge untapped potential in optimization technology to improve decisions either at development time or even at run time by applying optimization techniques. This could lead to achieving the full vision of artificial intelligence. Related stories Insights in Action Podcast: Identifying the core capabilities your business needs to get MLOps right New Tech Talks Daily Podcast: Machine learning and AI in business — investment trends pre- and post-pandemic In digital transformation, small wins lead to big outcomes

In the not so distant past, consumers mostly interacted with their banks in person. Retail customers, for instance, waited in line to make a deposit or talk to a banker. And though the branch may have been busy, a moving line gave comfort to customers that the wait wouldn't be much longer. However, customer expectations in the digital era are dramatically different. According to Experian's new research, one in three customers will abandon a transaction if they have to wait more than 30 seconds, especially when accessing bank accounts. And that's just the tip of the iceberg. When it comes to the digital experience, consumers increasingly want seamless service at every point of their journey. Now, as the Covid-19 crisis continues to accelerate digital demand, financial institutions face more and more customers with similar if not greater expectations. Expectations for things like personalized products, contextual lending decisions, and offline-online seamlessness. And those organizations that understand these evolving needs and deploy cloud-based decision management to ensure they meet them will likely be the winners in this new world. Right here, right now Banking digital transformation was already underway before the pandemic began. Most retail banks provided some customer-facing app. In efforts to automate and streamline business processes, many organizations have also started to migrate their backend infrastructure from on-premise software to the cloud. The pandemic, though, ramped up the demand for everything digital seemingly overnight. Consider that consumer adoption of mobile wallets has jumped 11% since July, largely due to increased contactless in-payments. In the height of the crisis, customers turned to online platforms for financial assistance, from federal loans and grants to mortgage relief and credit applications to small business loans. Businesses that had already migrated to cloud-based solutions were able to scale their response to meet that growth. But that those hadn't? They faced the combined challenge of needing to scale existing services to serve the influx of online customers while simultaneously adding new digital capabilities. As a result, some organizations have ended up playing catch up with their digital offerings. Experian research shows, though, that it's a race worth finishing. Sixty percent of customers say they have higher expectations of their digital experience now than they did before the pandemic. To be sure, the crisis will end. Those expectations, however, are here to stay. A glimpse of the future Banks may see fewer customers in person, but that doesn't mean their service can't be personal. The data analytics features of cloud-based decision management software allow businesses to know more about their customers, providing personalized offers and services right when customers need them most. One bank we work with in India provides an ideal example. They've leveraged deep analytics and decisioning solutions to accelerate their online loan approval process from days down to seconds. They're no longer turning people away who are good candidates for loans. And they've increased their lending without having to take on additional risk. It's a win-win that reveals how organizations can leverage technology to satisfy customer expectations during the height of a crisis and continue to in a post-Covid reality. With cloud-based solutions, organizations can become 100% customer-centric, both in convenience and personalization. The data gives financial institutions a holistic view of their customers, enabling them to anticipate needs and tailor solutions to the individual. Transformation and soon No organization is going to digitally transform overnight. But given the urgency of the demand, there are proven ways to improve their digital customer experience sooner rather than later. Small-to-mid-sized organizations, for instance, should consider out-of-the-box Software-as-a-Service (SaaS) solutions. These offer pre-determined, high-demand use cases such as online eligibility checks and customer acquisition tools. Organizations can modify these solutions to meet specific market needs while saving time on ramping up a fully custom solution. Additionally, even with the imperative to meet the digital demand, it's important to remember that proper planning leads to successful cloud migrations. Consider all the possibilities of what could go wrong and right in terms of incident management, customer service, links to data sources, and more. Rehearse your transition as much as feasible. The preparation may add a bit of time on the front end, but you'll decrease the likelihood of significant disruption when you do migrate and that's worth the effort. The march toward an increasingly digital customer experience only moves in one direction: forward. The pandemic may have pushed financial institutions to speed up their transition to cloud-based decision management, perhaps a bit earlier than some anticipated. But the outcome of a proactive, data-driven organization centered on serving customers promises to be better for everyone. Related stories: New research available: The continued impact of Covid-19 on consumer behaviors and business strategies  Automating fairness: Using analytics to help consumers in a pandemic era In digital transformation, small wins lead to big outcomes 

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