Tag: optimization

This article was updated on March 12, 2024. The number of decisions that a business must make in the marketing space is on the rise. Which audience to target, what is the best method of communication, which marketing campaign should they receive? To stay ahead, a growing number of businesses are embracing artificial intelligence (AI) analytics, machine learning, and mathematical optimization in their decisioning models and strategies. What is an optimization model? While machine learning models provide predictive insights, it’s the mathematical optimization models that provide actionable insights that drive decisioning. Optimization models factor in multiple constraints and goals to leave you with the next best steps. Each step in the optimization process can significantly improve the overall impact of your marketing outreach — for both you and your customers. Using a mathematical optimization software, you can enhance your targeting, increase response rates, lower cost per acquisition, and drive engagement. Better engagement can lead to stronger business performance and profitability. Here are a few key areas where machine learning and optimization modeling can help increase your return on investment (ROI): Prospecting: Advanced analytics and optimization can be used to better identify individuals who meet your credit criteria and are most likely to respond to your offers. Taking this customer-focused approach, you can provide the most relevant marketing messages to customers at the right time and place. Cross-sell and upsell: The same optimized targeting can be applied to increase profitability with your existing customer base in cross-sell and up-sell opportunities. Gain insights into the best offer to send to each customer, the best time to send it, and which channel the customer will respond best to. Additionally, implement logic that maintains your customer contact protocols. Retention: Employing optimization modeling in the retention stage helps you make quicker decisions in a competitive environment. Instantly identify triggers that warrant a retention offer and determine the likelihood of the customer responding to different offers. LEARN MORE: eBook: Debunking the top 5 myths about optimization Gaining insight and strengthening decisions with our solutions Experian’s suite of advanced analytics solutions, including our optimization software, can help improve your marketing strategies. Use our ROI calculator to get a personalized estimate of how optimization can lift your campaigns without additional marketing spend. Start by inputting your organization’s details below. initIframe('62e81cb25d4dbf17c7dfea55'); Learn more about how optimization modeling can help you achieve your marketing and growth goals. Learn more  

While today’s consumers expect a smooth, frictionless digital experience, many financial institutions still rely on outdated technology and manual reviews to acquire new customers. These old processes can prevent lenders from making accurate and timely credit decisions, leading to lost opportunities, revenue, and goodwill. By optimizing their customer acquisition strategies, financial institutions can allocate their resources effectively and say yes to consumers faster. This guide will walk you through the current challenges facing customer acquisition and how robust optimization strategies can help. Current challenges in customer acquisition To stay competitive and engage high-value customers, you’ll need an efficient customer acquisition process that weeds out both fraudulent actors and risky consumers. However, achieving this balancing act comes with a unique set of challenges. Because today’s consumers can access goods and services almost anywhere online at any time, more than 54 percent of customers expect a heightened digital and frictionless experience. Failing to meet this expectation can lead to huge losses for lenders. Some of the most common challenges in customer acquisition include: Although 52 percent of consumers prefer digital banking options over visiting branches in person, many lenders still rely on paper documents, which can add weeks to the onboarding process. Requiring consumers to provide substantial information about themselves during an application process can lead to abandoned applications. 67 percent of consumers will leave an application if they experience complications. Verifying consumer identities is growing increasingly important. In fact, about 35 percent of customers drop out of digital onboarding because their identity can't be confirmed. Poorly defined campaign planning can cause businesses to market to the wrong population segments, resulting in wasted time and resources. What is optimization for customer acquisition?  Customer acquisition optimization is the process of implementing new methods and solutions to make acquiring new customers more efficient and cost-effective. For lenders, this means streamlining steps in the credit decisioning process to focus on the right prospects and reduce friction. What types of processes can be optimized for customer acquisition?  You might be surprised just how many processes can be optimized for customer acquisition. Here are just a few examples: Having a holistic view of consumers allows you to take the guesswork out of targeting so you can better identify and engage high-potential customers. Utilizing predictive and lifestyle data enables you to pinpoint a more precisely segmented audience for marketing. Digital application solutions that reach across multiple channels, allowing applicants to leave one channel and pick up right where they left off in another. Real-time identity verification and fraud detection during onboarding and after, helping expedite approvals and mitigate risks. Utilizing API integration to leverage multiple metrics beyond credit scores when screening applicants' financial situation. Building custom risk models that pair to your existing data so you can say yes to more customers and better manage portfolio risk. Benefits of customer acquisition optimization Optimization can bring numerous benefits to your business, providing a faster return on investment. Here are some examples. By better pinpointing your marketing through predictive and lifestyle data, you can achieve increased conversions. Faster onboarding with less friction helps retain more customers. Real-time fraud detection and identity verification reduce customer roadblocks, allowing you to realize significant growth. Custom risk models and decisioning platforms can pair your data with additional data elements, providing more than just a credit score rating for your applicants. This can help you say yes to more customers. Using AI and machine learning tools will reduce the need for manual reviews and thus increase booking rates and applications. A real-life example of these benefits can be found with the Michigan State University Federal Credit Union (MSUFCU.) With over $7.2 billion in assets and 330,000 members, the client was manually reviewing all its applications. Experian reviewed the client's risk levels and approvals, comparing their risk and bankruptcy scores to determine which were most predictive. This analysis led Experian to recommend a new decisioning platform (PowerCurve Originations®) for instant credit decisions, an alternative data score tool, and Experian Advisory Services for risk-based pricing. After implementing these optimization solutions, MSUFCU saw a 55 percent increase in average monthly automations, four times improved online application response time and began competing more effectively in the marketplace. How Experian can help Experian offers a number of customer acquisition tools, allowing companies to be more responsive in an increasingly competitive market, while still reducing fraud risk. These tools include: Acquisition optimization marketing Experian offers a web-based platform that lets clients manage their marketing efforts all in the same place. You can upload and enhance client files, identify lookalike prospects, and use firmographic and credit data to get a holistic view of your clients and your prospects. Data-driven acquisition and decisioning engine PowerCurve Originations® is a data-driven decisioning engine that accepts applications from multiple channels, automates data collection and verification and proactively monitors decision results. It's flexible enough to reach across multiple channels, letting customers set aside their application in one digital channel and resume where they left off in another. It also provides businesses with access to comprehensive data assets, proactive monitoring and streamlined development with minimal coding. Enhanced fraud detection and identity verification Experian's Precise ID® is a risk-based fraud detection and prevention platform that provides analytics to accurately verify customers and mitigate fraud loss behind the scenes, ensuring a smoother onboarding process. Robust consumer attributes for better customized models Experian gives clients access to a wider berth of consumer attributes, helping you better screen applicants beyond just looking at credit scores. Trended 3DTM attributes let you uncover unique patterns in consumers' behavior over time, allowing you to manage portfolio risk, build better models and determine the next best actions. Premier AttributesSM aggregates credit data at the most granular and meaningful levels to provide clear insights into consumer credit behavior. It encompasses more than 2,100 attributes across 51 industries to help you develop highly predictive custom models. Enterprise-wide credit decisioning engine Experian's enterprise-wide credit decision platform lets you combine machine learning with proprietary data to return optimized decisions and quickly respond to requests. Robust credit decisioning software lets you convert data into meaningful actions and strategies. With Experian's machine learning decisioning options, companies are realizing a 25 percent reduction in manual reviews, a 25 percent increase in loan and credit applications and a 26 percent increase in booking rates. Highly predictive custom models Experian's Ascend Intelligence ServicesTM can help you create highly predictive custom models that create sophisticated decisioning strategies, allowing you to accurately predict risk and make the best decisions fast. This end-to-end suite of solutions lets you achieve a more granular view of every application and grow portfolios while still minimizing risk. Experian can help optimize your customer acquisition Experian provides a suite of decisioning engines, consumer attributes and customized modeling to help you optimize your customer acquisition process. These tools allow businesses to better target their marketing efforts, streamline their onboarding with less friction and improve their fraud detection and mitigation efforts. The combination can deliver a powerful ROI. Learn more about Experian's customer acquisition solutions. Learn more

Breaking down, rethinking, and optimizing your debt collection recovery process can be complicated — but you risk falling behind if you don't invest in your business. From managing live agents to unlocking the latest machine-learning models, there are different options and routes you can take to improve recovery rates.  Debt collection challenges in 2023 Collection agencies have embraced digitization. The benefits are numerous — cost savings, streamlined processes, and improved compliance, to name a few. However, digital tools aren't cure-alls, and they can even create new challenges if you're not careful. Maintaining accurate consumer data: Quickly reaching consumers can be difficult during times of economic uncertainty. Increased access to data can help you overcome this challenge, but only if you can manage and understand the information. If you simply turn on the metaphorical data streams, you could find yourself drowning in duplicate and erroneous entries.Keeping up with rising delinquencies: Delinquency rates steadily rose throughout 2022.1 Although rates may level out for some types of accounts in 2023, collection agencies need a plan for dealing with the potential increased volume. At the same time, continued low unemployment rates could make it difficult to hire and retain agents. Managing a tight budget: Recession worries also have companies rethinking expenses, which can impact your ability to increase head count and invest in technology. Finding effective trade-offs is going to be important for debt collection process optimization.Staying compliant: We've seen some major changes over the last few years, but there's no time to rest — debt collectors always need to be aware of new state and federal regulations. Digitization might make compliance more difficult if you're now managing an increasing amount of personal information or using text messages (or other omni-channels) to contact consumers. WATCH:Keeping pace with collections compliance changes Five ways to enhance your debt collection process Here are five ways that debt collectors can overcome today's challenges and take advantage of new opportunities.  1. Leverage clean data Continuously updating and checking the accuracy of your data can help increase right-party contact rates. But don't rely on your internal data and basic internet searches or public records. Leading data and skip-tracing services can give you access to additional data from credit bureaus, alternative financial services, collateral records, business listings and other helpful sources. Some skip-tracing tools can continuously verify and update contact information. They can also rank contact records, such as phone numbers, to save your agent's time. And identify consumers in a protected status such as bankrupt, deceased, and active military) and require special handling to help you stay compliant. 2. Implement advanced analytics and automation High-quality data can also be the foundation for a data-driven approach to collections.  Use collections-specific models: Although credit risk scores can be a piece of the debt collection puzzle, debt collection recovery models are often a better fit. You may be able to use different models to score accounts based on exposure, risk, willingness to pay or behavioral factors. Segment accounts: Increased insights and models also allow you to more precisely segment accounts, which can help you handle larger volumes with fewer resources. For instance, you can more accurately determine which accounts require an agent's personal touch, which can move forward with an automated experience and which should go to the back of your queue.  The data-driven approach also allows you to increasingly automate your collections — which can help you deal with rising delinquency rates in the face of a tight labor market and budget constraints. 3. Know when and how to make contact Segmentation and advanced analytics can tell you who and when to contact, but you also have to be mindful of how you reach out. Letters, calls, emails and texts can all be effective in the right circumstances, but no single option will always be best. For example, a text could be ideal when contacting Gen Z, but a call might work best for Baby Boomers. That's neither novel nor surprising, but it is important to stay up to date with the latest trends and preferences. Ideally, you reach people on their preferred channel at an appropriate time. You may also need to continually test, monitor and refine your process, especially if you want to increase automation.  READ:Digital Debt Collection Future white paper 4. Offer financially appropriate treatments In addition to picking the right communication channel, consider the payment options you offer consumers. Various payment plans, settlements and policies can directly affect your recovery rates — and what performed best in previous years might not make sense anymore. Chatbots and virtual negotiators can also help improve recovery rates without straining your agents' time. And for accounts that will likely self-cure, automated texts or emails with links to self-service portals could be an ideal solution. Expanding payment methods, such as accepting payments from digital wallets when you're sending a text message, could also make sense. However, you want to be sure you're not wasting time or money by contacting consumers who don't have the means to make a payment. Instead, set those accounts aside for now, but monitor them for changes that could indicate their financial situation has changed — such as a new credit line. Then, try to offer a solution that will likely fit the consumer's circumstances. 5. Invest in your live agents Modern debt management and collection systems focus on digitization and automation, and these can improve recovery rates. But don't forget about your front-line agents. There will always be times when a personal touch gets you further than an automated message. Continued training and ongoing recognition can be important for retaining top performers, maintaining compliance and increasing agents' effectiveness.  Partner for success Implementing an efficient and effective collections strategy can require a lot of work, but you don't have to go at it alone. Experian offers various debt collection solutions that can help optimize processes and free up your organization's resources and agents' time. Tap into our industry-leading data sources — including traditional credit data, alternative financial data and over 5,000 local phone exchange carriers — to find, update and verify account information. Available on the cloud or with secure file transfers, the TrueTrace™ and TrueTrace Live™ tools have led to a 10 percent lift in right-party contact rates compared to competitors. When it comes to optimizing outreach, you can prioritize accounts with over 60 industry-specific debt recovery scores via PriorityScore for CollectionsSM. Or work with Experian to create custom models for your organization. For an end-to-end decisioning solution, our AI-driven PowerCurve® Collections solution draws from internal and external data to determine the proper customer contact frequency, channel and treatment options, including self-service portals. Create your own strategies and workflows and manage the entire process with a single dashboard, cloud-based access and integrated reports. Learn more about Experian's debt collection process solutions 1Experian. (February 2023). Credit Scores Steady as Consumer Debt Balances Rise in 2022

“I saw an opportunity to create change instead of asking for it.” Day 2 was charged up with new technology; new ideas; and new, clearer visions of where we can drive change across our industries. Jeff Softley, President, Direct to Consumer, Experian Consumer Services, illustrated how the consumer is at the center of Experian’s business with countless statistics and how our consumer advocacy drives our focus, growth and mission. Wil Lewis, Global Chief of Diversity, Equity and Inclusion; Hiq Lee, President of Business Information Services; and Alex Lintner, Group President, Consumer Information Services, engaged in a panel discussion centered on reimagining inclusion.         Keynote: Allyson Felix Allyson Felix, five-time Olympian and most decorated Track & Field athlete, kicked off the day with an inspiring keynote touching on her athletic career, taking challenges head-on and using our platforms to make an impact. Felix, who is racing in the first race of her final season this weekend, is a tireless advocate, life-long learner, who seeks to empower others. “We can all start where we are,” she said. “Small things turn into big things.”       Day 2 session highlights From the breakout sessions, the theme of disruption was evident. We dove into how prescreen and prequalification have evolved, a demand that many must adapt to deliver in the post-pandemic world. Financial inclusion was a topic covered across the board, as were the strategies to be enacted to bolster these financial inclusion drivers. One such area addressed was how the rapidly growing buy now, pay later industry advances financial access and inclusion efforts. And speaking of evolution, retention must evolve as well — we heard how retention, recapture and risk strategies are transforming, particularly in the mortgage servicing space. Rapid Model Development and Deployment - Feedback from businesses reflects organizations’ desires for flexible deployment options, flexible integration with existing tech stacks, open source technology and the ability to incorporate multiple data providers. Today’s solutions address that feedback as well as solve for the most rampant market challenges in new, innovative ways.   Strategy optimization with Artificial Intelligence and Machine Learning - Over 50% of financial institutions surveyed are using AI/ML in at least one department. Challenges include data management, operation, evolving the analytics program. ML/AI starts with proper data management. For optimization, templatizing ML frameworks is a necessity.   ID Verification, Authentication and Fraud - There were $56B in identity fraud losses in 2020, $13B of which were traditional identity fraud losses and $43B related to identity fraud scams. Leveraging strategies is necessary to maintain the critical balance required for identity verification and fraud – mitigating losses and risk exposure, drive optimal customer experience, maintain regulatory compliance.   Maximizing Customer Value - The monthly data refresh is a thing of the past. When reimagining account review for risk and marketing purposes, remaining agile is key with increased data freshness for operational efficiency. Keynote: Ashton Kutcher The energy, insights and ideas have been reverberating throughout the venue for the past 48 hours, which set the stage for Ashton Kutcher’s closing keynote. The Chicago Bears fan talked about his career, how hard work wasn’t an option when he was growing up and how part of his assessment process for potential investments – determining whether they create efficiencies in the market – he sometimes thinks of a long-standing, personal benchmark – the air nailer. He talked about his philanthropy efforts, the mission behind his company Thorn, and the ability for people to impact change and achieve "a sense of agency" over the outcome of the future. “That’s the human spirit. That’s the spark that exists – that people understand that you can sit in despair, or you can do something,” he said. It has been an amazing two days – we can’t wait for Vision 2023!

At Experian, we know that financial institutions, fintechs and lenders across the entire spectrum – small, medium and large, are further exploring and adopting AI-powered solutions to unlock growth and improve operational efficiencies. With increasing competition and a dynamic economy, AI-driven strategies across the entire customer lifecycle are no longer a nice to have, they are a must. Our dedication to delivering on this need for our clients is why we are thrilled to be recognized as a Fintech Breakthrough Award winner for the fifth consecutive year. Experian’s Ascend Intelligence ServicesTM (AIS) platform hosts a suite of analytics solutions and has been named “Best Consumer Lending Product” in the sixth annual FinTech Breakthrough Awards. This awards program is conducted by FinTech Breakthrough, an independent market intelligence organization that recognizes the top companies, technologies and products in the global fintech market today. This is the second consecutive year that AIS has been recognized with a FinTech Breakthrough Award, previously being selected for the “Consumer Lending Innovation Award” in 2021. “Winning another award from FinTech Breakthrough is a fantastic validation of the success and momentum of our Ascend Intelligence Services suite. Now more than ever, the world is in a state of constant change and companies are being reactive, with data scientists spending too much time on manual, repetitive data-wrangling tasks, at a time when they cannot afford to do so,” said Shri Santhanam, Experian’s executive vice president and general manager of Global Analytics and AI. “Companies need to be able to rapidly develop and deploy ML-powered models in an agile way at low cost. We are now able to offer this to more lenders no matter their size.” With AIS, Experian can empower financial services firms to make the best decisions across the customer life cycle with rapid model and strategy build, seamless deployment, optimization and continuous monitoring. The AIS suite is comprised of two key solution models: Ascend Intelligence Services Acquire is a managed services offering that enables financial institutions to increase approval rates and control bad debt by acquiring the right customers and providing the best offers. This is accomplished through a rapid AI/ML model build that will help better quantify the risk of an individual applicant. Next, a mathematically optimized decision strategy is designed to provide a more granular view of the applicant and help make the best decision possible based on the institution’s specific business goals and constraints. The combination of the AI/ML model and optimized decision strategy provides increased predictive power that mitigates risk and allows more automated decisions to be made. The model and strategy are seamlessly deployed to help deliver business value quickly. Ascend Intelligence Services™ Limit enables financial institutions to make the right credit limit decisions at account origination and during account management. Limit uses Experian’s data, predictive risk and balance models and our powerful optimization engine to design the right credit limit strategy that maximizes product usage, while keeping losses low. To learn more about how Ascend Intelligence Services can support your business, please explore our solutions page. Learn more For a list of all award winners selected for the 2022 FinTech Breakthrough Awards, click here.

The collections landscape is changing as a result of new and upcoming legislation and increased expectations from consumers. Because of this, businesses are looking to create more effective, consumer-focused collections processes while remaining within regulatory guidelines. Our latest tip sheet has insights that can help businesses and agencies optimize their collections efforts and remain compliant, including:   Start with the best data Keep pace with changing regulations Focus on agility Pick the right partner Download the tip sheet to learn how to maximize your collections efforts while reducing costs, avoiding reputational damage and fines, and improving overall engagement. Download tip sheet

Intuitively we all know that people with higher credit risk scores tend to get more favorable loan terms. Since a higher credit risk score corresponds to lower chance of delinquency, a lender can grant: a higher credit line, a more favorable APR or a mix of those and other loan terms. Some people might wonder if there is a way to quantify the relationship between a credit risk score and the loan terms in a more mathematically rigorous way. For example, what is an appropriate credit limit for a given score band? Early in my career I worked a lot with mathematical optimization. This optimization used a software product called Marketswitch (later purchased by Experian). One caveat of optimization is in order to choose an optimal decision you must first simulate all possible decisions. Basically, one decision cannot be deemed better than another if the consequences of those decisions are unknown. So how does this relate to credit risk scores? Credit scores are designed to give lenders an overall view of a borrower’s credit worthiness. For example, a generic risk score might be calibrated to perform across: personal loans, credit cards, auto loans, real estate, etc. Per lending category, the developer of the credit risk score will provide an “odds chart;” that is, how many good outcomes can you expect per bad outcome. Here is an odds chart for VantageScore® 3 (overall - demi-decile). Score Range How Many Goods for 1 Bad 823-850 932.3 815-823 609.0 808-815 487.6 799-808 386.1 789-799 272.5 777-789 228.1 763-777 156.1 750-763 115.6 737-750 85.5 723-737 60.3 709-723 45.1 693-709 33.0 678-693 24.3 662-678 18.3 648-662 14.1 631-648 10.8 608-631 7.9 581-608 5.5 542-581 3.5 300-542 1.5 Per the above chart, there will be 932.3 good accounts for every one “bad” (delinquent) account in the score range of 823-850. Now, it’s a simple calculation to turn that into a bad rate (i.e. what percentage of accounts in this band will go bad). So, if there are 932.3 good accounts for every one bad account, we have (1 expected bad)/(1 expected bad + 932.3 expected good accounts) = 1/(1+932.3) = 0.1071%. So, in the credit risk band of 823-850 an account has a 0.1071% chance of going bad. It’s very simple to apply the same formula to the other risk bands as seen in the table below. Score Range How Many Goods for 1 Bad Bad Rate 823-850 932.3 0.1071% 815-823 609.0 0.1639% 808-815 487.6 0.2047% 799-808 386.1 0.2583% 789-799 272.5 0.3656% 777-789 228.1 0.4365% 763-777 156.1 0.6365% 750-763 115.6 0.8576% 737-750 85.5 1.1561% 723-737 60.3 1.6313% 709-723 45.1 2.1692% 693-709 33.0 2.9412% 678-693 24.3 3.9526% 662-678 18.3 5.1813% 648-662 14.1 6.6225% 631-648 10.8 8.4746% 608-631 7.9 11.2360% 581-608 5.5 15.3846% 542-581 3.5 22.2222% 300-542 1.5 40.0000%   Now that we have a bad percentage per risk score band, we can define dollars at risk per risk score band as: bad rate * loan amount = dollars at risk. For example, if the loan amount in the 823-850 band is set as $10,000 you would have 0.1071% * $10,000 = $10.71 at risk from a probability standpoint. So, to have constant dollars at risk, set credit limits per band so that in all cases there is $10.71 at risk per band as indicated below. Score Range How Many Goods for 1 Bad Bad Rate Loan Amount $ at Risk 823-850 932.3 0.1071%  $   10,000.00  $   10.71 815-823 609.0 0.1639%  $     6,535.95  $   10.71 808-815 487.6 0.2047%  $     5,235.19  $   10.71 799-808 386.1 0.2583%  $     4,147.65  $   10.71 789-799 272.5 0.3656%  $     2,930.46  $   10.71 777-789 228.1 0.4365%  $     2,454.73  $   10.71 763-777 156.1 0.6365%  $     1,683.27  $   10.71 750-763 115.6 0.8576%  $     1,249.33  $   10.71 737-750 85.5 1.1561%  $        926.82  $   10.71 723-737 60.3 1.6313%  $        656.81  $   10.71 709-723 45.1 2.1692%  $        493.95  $   10.71 693-709 33.0 2.9412%  $        364.30  $   10.71 678-693 24.3 3.9526%  $        271.08  $   10.71 662-678 18.3 5.1813%  $        206.79  $   10.71 648-662 14.1 6.6225%  $        161.79  $   10.71 631-648 10.8 8.4746%  $        126.43  $   10.71 608-631 7.9 11.2360%  $          95.36  $   10.71 581-608 5.5 15.3846%  $          69.65  $   10.71 542-581 3.5 22.2222%  $          48.22  $   10.71 300-542 1.5 40.0000%  $          26.79  $   10.71   In this manner, the output is now set credit limits per band so that we have achieved constant dollars at risk across bands. Now in practice it’s unlikely that a lender will grant $1,683.27 for the 763 to 777 credit score band but this exercise illustrates how the numbers are generated. More likely, a lender will use steps of $100 or something similar to make the credit limits seem more logical to borrowers. What I like about this constant dollars at risk approach is that we aren’t really favoring any particular credit score band. Credit limits are simply set in a manner that sets dollars at risk consistently across bands. One final thought on this: Actual observations of delinquencies (not just predicted by the scores odds table) could be gathered and used to generate a new odds tables per score band. From there, the new delinquency rate could be generated based on actuals. Though, if this is done, the duration of the sample must be long enough and comprehensive enough to include both good and bad observations so that the delinquency calculation is robust as small changes in observations can affect the final results. Since the real world does not always meet our expectations, it might also be necessary to “smooth” the odds-chart so that its looks appropriate.

This is the fourth in a series of blog posts highlighting optimization, artificial intelligence, predictive analytics, and decisioning for lending operations in times of extreme uncertainty. The first post dealt with optimization under uncertainty, the second with predicting consumer payment behavior, and the third with validating consumer credit scores. This post describes some specific Experian solutions that are especially timely for lenders strategizing their response to the COVID Recession. Will the US economy recover from the pandemic recession?  Certainly yes. When will the economy recover? There is a lot more uncertainty around that question. Many people are encouraged by positive indicators, such as the initial rebound of the stock market, a return of many of the jobs lost at the beginning of the pandemic, and a significant increase in housing starts. August’s retail spending and homebuilder confidence are very encouraging economic indicators. Other experts doubt that the “V-shaped” recovery can survive flare-ups of the virus in various parts of the US and the world, and are calling for a “W-shaped” recovery.  Employment indicators are alarming: many people remain out of work, some job losses are permanent, and there are more initial jobless claims each week now than at the height of the Great Recession. Serious hurdles to economic recovery may remain until a vaccine is widely available: childcare, urban transportation, and global trade, for example. I’m encouraged by the resilience of many of our country’s consumer lenders. They are generally responding well to these challenges. If past recessions are a guide, some lenders will not survive these turbulent times. This time, many lenders—whether or not they have already adopted the CECL accounting standards—have been increasing allowances for their anticipated credit losses. At least one rating agency believes major banks are prepared to absorb those losses from earnings.  The lenders who are most prepared for the eventual recovery will be those that make good decisions during these volatile times and take action to put themselves in the best position in anticipation of the recovery that will certainly follow. The best lenders are making smart investments now to be prepared to capitalize on future opportunities. Experian’s analytics and consulting experts are continuously improving our suite of solutions that help consumer lenders and others assess consumer behavior and respond quickly to the rapidly fluctuating market conditions as well as changing regulations and credit reporting practices. Our newly announced Economic Response and Recovery Suite includes the ABCD’s that lenders need to be resilient and competitive now and to prepare to thrive during the eventual recovery: A – Analytics. As I’ve written about in prior blog posts, data is a prerequisite to making good business decisions, but data alone is not enough. To make wise, insightful decisions, lenders need to use the most appropriate analytical techniques, whether that means more meaningful attributes, more predictive and compliant credit scores, more accurate and defensible loss forecasting solutions, or optimization systems that help develop strategies in a world where budgets, regulations, and other constraints are changing. For example, Experian has released a set of Spotlight 2020 Attributes that help consumer lenders create a positive experience for customers who have received an accommodation during the pandemic. In many cases motivated by the new race to improve customer experience online, and in other cases as a reaction to new and creative fraud schemes, some clients are using this period as an opportunity to explore or deploy ethical and explainable Artificial Intelligence. B – Business Intelligence. Credit bureaus like Experian are uniquely situated to understand the impact of the COVID recession on America’s consumers. With impact reports, dashboards, and custom business intelligence solutions, lenders are working during the recession to gain an even better understanding of their current and prospective customers. We’re helping many of them to proactively help consumers when they need it most. For example, lenders have turned to us to understand their customer’s payment hierarchy—which bills they pay first when times are tough. Our free COVID-19 US Business Risk Index helps make lending options available to the businesses who need them most. And we’ve armed lenders with recommendations for which of our pre-existing attributes and scores are most helpful during trying times. Additional reporting tools such as the Auto Market Tracker, Ascend Market Insights Dashboard, and the weekly economic update video provide businesses with information on new market trends—information that helps them respond during the recession and promises to help them grow during the eventual recovery. C – Consulting. It’s good to turn data into information and information into insight, but how do these lenders incorporate these insights in their business strategies? Lenders and other businesses have been turning to Experian’s analytics and Advisory services consultants to unlock the information hidden in credit and other data sources—finding ways to make their business processes more efficient and more effective while developing quick response plans and more long-term recovery strategies. D – Delivery.  Decision science is the practice of using advanced analytics, artificial intelligence, and other techniques to determine the best decision based on available data and resources. But putting those decisions into action can be a challenge. (Organizations like IBM and Gartner estimate that a great majority of data science projects are never put into production.) Experian technologies—from our analytics platform to our attribute integration and decision management solutions ensure that data-driven decisions can be quickly implemented to make a real difference. Treating each customer optimally has a number of benefits—whether you are trying to responsibly grow your portfolio, reduce credit losses and allowances, control servicing costs, or simply staying in compliance during dynamic times. In the age of COVID, IT departments have placed increased priority on agility, security, customer experience, and cost control, and appreciate cloud-first approach to deploying analytics. It’s too early to know how long this period of extreme uncertainty will last. But one thing is certain: it will come to an end, and the economy will recover someday. I predict that many of the companies that make the best use of data now will be the ones who do the best during the recovery. To hear more ways your organization can navigate this downturn and the recovery to follow, please watch our on-demand webinar and check out our Economic Response and Recovery Suite. Watch the Webinar

This is the first to a series of blog posts highlighting optimization, artificial intelligence, predictive analytics, and decisioning for lending operations in times of extreme uncertainty.   Like all businesses, lenders are facing tremendous change and uncertainty in the face of the COVID-19 crisis.  While focusing first on how to keep their employees and customers safe during the new normal, they are asking how to make data-driven decisions in this new environment.  It’s only natural that business people are skeptical about whether analytics will work in a situation like today's – in which the data deviate from all historical precedents.  Certainly, nobody predicted, for example, that the number of loans with forbearance requests would increase by over 1000% during each two-week period in March. Can anyone possibly make an optimized decision when things are changing so quickly and when so many things are unknown?   Prescriptive analytics – also known as mathematical optimization – is the practice of developing a business strategy to achieve a business objective subject to capacity and other constraints, often using a demand forecast. For example, banks use optimization software to develop marketing and debt management strategies to run their lending operations.  But what happens when the demand forecast might be wrong, when the constraints change quickly, and when decision-makers cannot agree on a single objective? The reality is that decisionmakers have to balance multiple competing objectives related to many different stakeholders. And, especially during the COVID-19 crisis and the period of change that will certainly follow, they have to do so in the face of uncertainty.   Let's discuss some of the methods that analysts use to control risk while optimizing lending practices during times like these. These techniques, collectively known as robust optimization and robust statistics, help lenders and other business people deal with the uncomfortable reality that we do not know what the future holds.     Consider a hypothetical bank or other lender servicing a portfolio of consumer loans and forecasting its loss performance in this environment. Management probably has several competing objectives: they want to improve service levels on their digital channel, they want to minimize credit and fraud losses, they're facing a reduced operating budget, and they're not certain how many employees they will have and which vendors will be able to provide adequate service levels. Furthermore, they anticipate new and unpredicted changes, and they need to be able to update their strategies quickly.   The mathematics can be quite technical, but Experian’s Marketswitch Optimization is user-friendly software to help businesspeople--not engineers--design and deploy optimal strategies for practices such as Account Management and Loan Originations while facing such a dynamic and uncertain environment. The bank's business analysts (not computer specialists or mathematicians) will use techniques such as these:   With Sensitivity Analysis, the analysts will explore the performance of their optimized Account Management, Collections, and Loan Originations strategies while considering possible changes in input variables.   Optimization Scenarios with Uncertainty (technically known as Stochastic Optimization) allow the managers and analysts to design operational strategies that control risk, particularly the bank’s exposure to probabilistic and worst-case scenarios.   Using Scenario Performance Analysis, the lender's team will validate and test their optimization scenarios against a variety of different data sets to understand how their strategies would perform in each case.   Model Quality Evaluation techniques help the credit risk managers compare model predictions against actual performance during a quickly changing economy.   Model impact analysis (related to Model Risk Management) helps senior leadership assess when it is time to invest in improving its statistical models.   Robust Model Calibration Analysis removes unjustifiable variations in the lender's predictive models to make their predictions more valid as things change over time.   These six advanced analytics techniques are especially helpful when developing business strategies for a time in which some values are unknown—including future unemployment levels, staffing budgets, data reporting practices, interest rates, and customer demands.  Business decisions can—and arguably must—be optimized during times of uncertainty. But during times like these, it is especially important that the analysts understand how and why to account for the uncertainty in both the data and the models.   Lenders, are you optimizing your servicing and debt management strategies? It has never been more important than now to do so--using the advanced techniques available to manage uncertainty mathematically. Learn more about how Marketswitch can help you solve complex business problems and meet organizational objectives. Learn more

  Debt management is becoming increasingly complex. People don’t answer their phones anymore. There are many, many communication channels available (email, text, website, etc.) and just as many preferences from consumers regarding how they communicate. Prioritizing how much time and effort to spend on a debtor often requires help from advanced analytics and machine learning to optimize those strategies. Whether you are manually managing your collections strategies or are using advanced optimization to increase recovery rates, we’ve got keys to help you improve your recover rates. Watch our webinar, Keys to unlocking debt management success, to learn about: Minimizing the flow of accounts into collections and ensuring necessary information (e.g. risk, contact data) is used to determine the best course of action for accounts entering collections Recession readiness – prepare for the next recession to minimize impact Reducing costs and optimizing collections treatment strategies based on individual consumer circumstances and preferences Increasing recovery rates and improving customer experience by enabling consumers to interact with your organization in the most effective, efficient and non-threatening way possible Watch on-demand now>

Customer experience strategies for success Sometimes it’s easier to describe something as the opposite of something else.  Being “anti-” something can communicate something meaningful. Cultural movements in the past have taken on these monikers:  consider the “anti-establishment” or “anti-war” movements.  We all need effective anti-virus protection.  And there are loads of skin products marketed as “anti-aging”, “anti-wrinkle”, or “anti-blemish.” But when you think about a vision for the customer experience that your company aspires to deliver, this approach of the “anti-X” falls flat. Would you want to aspire to basically “not stink?”  Would that inspire you and your team to run through walls to deliver on that grand aspiration? Would it motivate customers to stick with you, buy more of what you sell, and tell others about you? I think not…But it sure seems like many out there indeed do aspire to “not stink.” Sure, there are great companies out there who have a set a high standard for customer experience, placing it at the center of their strategies and their success. Some, like Zappos, started that way from the beginning.  Others, like The Ritz-Carlton, realized that they had lost their way and made the commitment to do the hard work of reaching and sustaining excellence. On the other hand, there are hundreds of firms who have a weak commitment to or even understanding of the importance of customer experience to their strategy and performance.  Their leaders may give lip service or just pay attention for a few days or hours following the release of reports from leading analysts and firms. They may have posters and slogans that talk about putting the customer first or similar platitudes. These companies probably even have talented and passionate professionals working tirelessly to improve the customer experience in spite of the fact that nobody seems to care much. What these firms lack is a clear customer experience strategy. As nature abhors a vacuum, customers and employees are free to infer or just guess at it.  Focusing on customer experience only when a report comes out – and paying special attention only when weak results put the firm near the bottom of the ranking leads people to conclude that all that really matters is to “not stink.”  In other words, don’t stand out for being bad…but don’t worry much about being good as it is not important to the company’s strategy or results. I think that this “don’t stink” implicit strategy helps explain a fascinating insight from a Forrester survey in 2013: “80% of executives believe their company is delivering a superior customer experience, yet in 2013 only 8% of companies surveyed received a top grade from their customers.”  Many leaders simply have not invested the energy and commitment necessary to define a real customer experience vision that reflects a deep understanding of the role that it plays in the company’s strategy.  Beyond setting that vision, there is a big and sustained commitment required to deliver on the vision, measure results, and continuously adjust as customer needs evolve. Like all journeys, a great customer experience starts with one step. Establishing a customer experience strategy is the first one – and “don’t stink” simply stinks as a strategy. Download our recent perspective paper to learn how exceptional customer experience can give companies the competitive edge they need in a market where price, products and services can no longer be a differentiator.

By: Staci Baker As the economy has been hit by the hardest recession since the Great Depression, many people wonder how and when it will recover.  And, once we start to see recovery, will consumer credit return to what it once was? In a recent Experian-Oliver Wyman Market Intelligence Report quarterly webinar, 70% of the respondents in a survey said they believe consumer debt will return to pre-2008 levels.  Clearly, many believe that consumer spending and borrowing will return, despite the fact that consumer credit card borrowing recently declined for the 24th straight month*. Assuming that this optimism is valid, what can credit card lenders do to evaluate the risk levels of potential customers as they attempt to grow their portfolios? For lenders, determining who needs credit, as well as whom to lend to in this economic environment, can be quite challenging.  However, there are many tools available to assist lenders in assessing credit risk and growing their portfolio. Many lenders look at a consumer’s credit score, such as the tri-bureau VantageScore, to evaluate their credit worthiness. By utilizing an individual’s VantageScore, a lender is able to determine potential customer risk levels. Another way to evaluate a consumer’s credit worthiness is to evaluate a population using credit attributes.  Based on the attributes a lender is looking for in their portfolio, they can see improvement in evaluating risk prediction in their portfolio using pre-determined attributes, especially those specifically designed for the credit card industry. There are also models that can help lenders predict when a consumer is likely to be in the market for a new loan or account. Experian’s In the Market Models provide lenders with product-specific segmentation tools that can be combined with risk scores to enhance the efficiency and effectiveness of their offers. To identify the optimal cross-sell and line management decisions based on an individual customer’s risk score and potential value, a lender can also utilize optimization tools.  Optimization, combined with a viable risk management strategy, can assist a lender to achieve a healthy portfolio growth in a highly constrained environment. Although lenders will need to determine the best method to meet their objectives, these are just a few of the many tools available that will assist them in correctly growing their lending portfolios. ____________________ * http://www.usatoday.com/money/economy/2010-10-07-consumer-credit_N.htm

By: Wendy Greenawalt Optimization has become somewhat of a buzzword lately being used to solve all sorts of problems. This got me thinking about what optimizing decisions really means to me? In pondering the question, I decided to start at the beginning and really think about what optimization really stands for. For me, it is an unbiased mathematical way to determine the most advantageous solution to a problem given all the options and variables. At its simplest form, optimization is a tool, which synthesizes data and can be applied to everyday problems such as determining the best route to take when running errands. Everyone is pressed for time these days and finding a few extra minutes or dollars left in our bank account at the end of the month is appealing. The first step to determine my ideal route was to identify the different route options, including toll-roads, factoring the total miles driven, travel time and cost associated with each option. In addition, I incorporated limitations such as required stops, avoid main street, don’t visit the grocery store before lunch and must be back home as quickly as possible. Optimization is a way to take all of these limitations and objectives and simultaneously compare all possible combinations and outcomes to determine the ideal option to maximize a goal, which in this case was to be home as quickly as possible. While this is by its nature a very simple example, optimizing decisions can be applied to home and business in very imaginative and effective means. Business is catching on and optimization is finding its way into more and more businesses to save time and money, which will provide a competitive advantage. I encourage all of you to think about optimization in a new way and explore the opportunities where it can be applied to provide improvements over business-as-usual as well as to improve your quality of life.  

By: Wendy Greenawalt Financial institutions have placed very little focus on portfolio growth over the last few years.  Recent market updates have provided little guidance to the future of the marketplace, but there seems to be a consensus that the US economic recovery will be slow compared to previous recessions. The latest economic indicators show that slow employment growth, continued property value fluctuations and lower consumer confidence will continue to influence the demand and issuance of new credit. However, the positive aspect is that most analysts agree that these indicators will improve over the next 12 to 24 months. Due to this, lenders should start thinking about updating acquisition strategies now and consider new tools that can help them reach their short and long-term portfolio growth goals. Most financial institutions have experienced high account delinquency levels in the past few years. These account delinquencies have had a major impact to consumer credit scores. The bad news is that the pool of qualified candidates continues to shrink so the competition for the best consumers will only increase over the next few years. Identifying target populations and improving response/booking rates will be a challenge for some time so marketers must create smarter, more tailored offers to remain competitive and strategically grow their portfolios. Recently, new scores have been created to estimate consumer income and debt ratios when combined with consumer credit data. This data can be very valuable and when combined with optimization (optimizing decisions) can provide robust acquisition strategies. Specifically, optimization / optimizing decisions allows an organization to define product offerings, contact methods, timing and consumer known preferences, as well as organizational goals such as response rates, consumer level profitability and product specific growth metrics into a software application. The optimization software will then utilize a proven mathematical technique to identify the ideal product offering and timing to meet or exceed the defined organizational goals.  The consumer level decisions can then be executed via normal channels such as mail, email or call centers. Not only does optimization software reduce campaign development time, but it also allows marketers to quantify the effectiveness of marketing campaigns – before execution. Today, optimization technology provide decision analytics accessible for organizations of almost any size and can provide an improvement over business-as-usual techniques for decisioning strategies. If your organization is looking for new tools to incorporate into existing acquisition processes, I would encourage you to consider optimization and the value it can bring to your organization.

By: Wendy Greenawalt The economy has changed drastically in the last few years and most organizations have had to reduce costs across their businesses to retain profits. Determining the appropriate cost-cutting measures requires careful consideration of trade-offs while quantifying the short- and long-term organizational priorities.  Too often, cost reduction decisions are driven by dynamic market conditions, which mandate quick decision-making. Due to this, decisions are made without a sound understanding of the true impact to organizational objectives. Optimization (optimizing decisions) can be used for virtually any business problem and provides decisions based on complex mathematics. Therefore, whether you are making decisions related to outsourcing versus staffing, internal versus external project development or specific business unit cost savings opportunities, optimization can be applied. While some analytical requirements exist to obtain the highest business metric improvements, most organizations have the data available that is required to take full advantage of optimization technology.  If you are using predictive models, credit attributes and have multiple actions that can be taken on an individual consumer, then, most likely, your organization can benefit from strategies in optimizing decisions. In my next few blogs, I will discuss how optimization / optimizing decisions can be used to create better strategies across an organization whether your focus is marketing, risk, customer management or collections.