The prediction process involves the following steps: To get expected, consistent results, keep iterating. Using Predictive Analysis to Improve Invoice-to-Cash Collection Sai Zeng IBM T.J. Watson Research Center Hawthorne, NY, 10523 saizeng@us.ibm.com Ioana Boier-Martin IBM T.J. Watson Research Center Hawthorne, NY, 10523 ioana@us.ibm.com Prem Melville IBM T.J. Watson Research Center Yorktown Heights, NY, 10598 pmelvil@us.ibm.com Conrad Murphy The insights we get fit into a broader vision of digital transformation—where we bring together people, data, technology, and processes in new ways to engage customers, empower employees, optimize operations, and transform business solutions. There were lots of reviews and test cycles to demonstrate the accuracy and the high level of security that we have. Whereas Predictive analytics uses advanced computational models and algorithms for intelligently building a forecast or prediction platform, for example, a commodities trader might wish to predict short-term movements in commodities prices, collection analytics, fraud detection etc. Post collections, analytics can help continually adjust collections strategy in line with a changing environment, such as spotlighting the products and accounts that require closer attention. But, for the best results, you need the proper data systems in place. We take this data and determine if there are other features that we need to build out of the data to improve the success of the model. The scores go into our Karnak database and are displayed in Power BI reports to collections teams. This new approach is more accurate and can extend to the entire debt management process. We have more than 1,000 trees. Predictive Analytics can also be used in the Debt Collection and Personal Lending industry â as it helps to create a 360 degree portrait of the client, taking into consideration more details than ever before â including sending patterns and even social media. Otherwise, we mark it as unlikely to be late. This website uses cookies to make your browsing experience more efficient and enjoyable. The user asks a question to the chatbot in plain English. Improving Debt Collection with Predictive Models FICO scores will be soon improved by predictive analytics. You can find out more about which cookies we are using or switch them off in settings. These are the technologies and components that we’re using for our solution: Figure 1. Using Azure Machine Learning for early detection of delayed payments. Karnak data goes into Azure SQL Database, and App Service connects to SQL Database to answer the bot’s questions. Santa Cruzâs predictive policing system on a tablet. Debt collection is one of the most complex portfolios that need multiple KPI iterations to recover lost revenue. Reach out to us for any queries related to: Supercharging the Collections Function through Predictive Analytics, How Enabling Virtual Finance Operations Can Help Organizations be Future-ready, Intelligent Automation: Re-engineering Transformation in Finance, Futuristic CFO: Making the Cut to ‘Digital Finance’, It is a reactive approach that makes no effort to understand the causes of delinquency and prevent delayed payments before they occur, It fails to take advantage of the advances in predictive analytics that have already transformed Business-to- Consumer (B2C) collections in industries such as payment cards and utilities. By analyzing as close to the data source as possible, users can reduce latency, receiving information and making subsequent decisions more quickly. When we onboard new customers, we can correlate certain trends to them quite accurately, based on what we’ve seen with other customers. The largest tree has 100 levels. This identifies high-risk accounts, along with forecasting the most effective treatment for each account. This is done by understanding that not all delinquent accounts are the same. To speed up the process of answering these recurring questions, we built a chatbot. Companies can also tailor customer communications and offer self-service options based on analytics-driven insights. The higher the level, the easier you will find the website to use. Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining. We use the eXtreme gradient boosting (XGBoost) algorithm—a machine learning method—to create decision trees that answer questions like who’s likely to pay versus who isn’t. For a provider of IT and communication services to the air transport industry, profiling debt on the basis of outstanding periods and amounts helped uncover customers who held up the greatest quantum of cash and were the slowest to pay. The collections team contacted every customer with basically the same urgency. Or suppose there’s a billing dispute. Staples gained customer insight by analyzing behavior, providing a complete picture of their customers, and realizing a 137 percent ROI. Figure 1 below shows the model that we built. Driving Microsoft's transformation with AI. If you don’t have someone who understands the business scenarios, and you don’t have much historical data, it’s harder. It also helps collectors focus attention away from accounts that do not need attention — such as those shown to consistently self-heal soon after the due date. Predictive analytics models combine multiple predictors, or quantifiable variables, into a predictive model. We asked things like: To help with these and other questions, we use data science and Microsoft Azure Machine Learning as the backbone of our solution. Data Science for Beginners compares an algorithm to a recipe, and your data to the ingredients. It is always better to understand the type and reason of delinquency from historic data and act proactively on the accounts showing similar type of characteristics. Figure 2. Aligned with our mission of digital transformation, these insights join data, technology, processes, and people in new ways—helping the collections team to optimize operations by focusing on customers who are likely to pay late. Credit and collections team members often come across the same questions over and over. This is called feature engineering, and we used this approach to create feature variables such as type of customer, customer tenure, purchase amount, and purchase complexity (products per order). JR: âBefore utilities rush headlong into predictive analytics, they should start with some good, old-fashioned descriptive analytics on their historic data. Speeding up collections has a big financial payoff. Predictive analytics is valuable not only during collections activities, but also in preceding and following stages. Predictive analytics is easier with ready-to-use software options that offer embedded predictive modeling capabilities. Azure Data Factory. For example, suppose an invoice is due on Saturday, or a customer in a particular country/region tends to pay late, and the average invoice is, say, $2,000. Long-term, high-volume customers and partners are rarely late, and can benefit a lot from payment automation. We used Bot Framework and Azure App Service. WNS provides us a blend of functional expertise and process capabilities which spans across our diverse portfolio. For example, we have integrated insights into several of our collection processes and some systems, but not all of them. ...we are obliged to ask your permission before placing any cookies on your computer. The collections function is in the spotlight today because of renewed focus on cash flow and revenue assurance. Beyond deciding which customers to contact first, we see customer trends related to invoice amount, industry, geography, products, and other factors. There are primarily three stages of collection, which can be broadly classified as the early stage, the mid-stage and the final stage of collection. We also get a valuable understanding of the factors or tendencies linked with customers who’ve paid versus those who haven’t. As predictive analytics transforms every aspect of business in a data-rich world, organizations stand to gain a major advantage by embracing its potential for debt collection. The company’s treasury team manages credit and collections for these transactions. It puts their names at the top of a list for the collectors, so that they can contact these customers earlier in the process. Repurpose that money for other short-term and long-term investments. Advanced collections strategies allow organizations to go deeper into a highly competitive marketplace in search of new business. With data science, Azure Machine Learning, and predictive analytics, we improve customer satisfaction, empower our collections team, optimize the efficiency and speed of our collection operations, and weâre more predictive and proactive. In our case, we had people with this knowledge and five years of historical data. For example, this person has a 1—they’re unlikely to pay on time. The following steps, as shown in Figure 3, show how the chatbot works: Now, field sales, operations, and collectors can see the latest information about customers they interact with and detect issues. Intellicus predictive debt collection analytics solution enables you to curb debts, predict collection, and enhance overall portfolio performance. Why is this understanding important? Considering the amount of revenue, you can safely assume that even small improvements in collection efficiency translate to millions of dollars. Predictive Analytics is , âWhen you use your historical data with statistical techniques and Machine Learning to make predictions â.. Predictive Analytics looks like a technological magic and If you want to learn how to do this Magic . And now to the stuff agencies seem a bit shy about. Azure Machine Learning also gives us a risk percentage score of how likely the customer is to pay on time. In combination with well-defined business processes, the adoption of technology for predictive analytics can have a significantly positive impact on an organizationâs ability to enhance collections efficiency. This approach allows for the collection of data and subsequent formulation of a statistical model, to which additional data can be added as it becomes available. We knew what business factors were important. We use the XGBoost algorithm to create decision trees that look at features. MICROSOFT MAKES NO WARRANTIES, EXPRESS OR IMPLIED, IN THIS SUMMARY. Predictive analysis helps marketing teams invest their resources wisely and set KPIs that align with total business value. But say you’re starting from scratch. We mostly contact only customers who need help paying. Revenue leakage is another key issue that collectors can work to diminish, keeping in mind that companies lose up to 15 percent of revenue to customer 1 deductions each year . Predictive analytics is valuable not only during collections activities, but also in preceding and following stages. Every year, Microsoft collects more than $100 billion in revenue around the world. The benefit is that we can focus on these customers. Improve customer satisfaction by reaching out to specific customers with a friendly reminder, while not bothering those who typically pay on time. This begs the question: if the business impact of a better performing collections function is so compelling, why aren't organizations turning collections challenges into cash flow and revenue assurance opportunities? Examples include: Table 1 shows what we used to do, compared to what we do now that we’re using Azure Machine Learning, for improving our credit and collections processes. The right approach uses forward-looking analytics to address both the 'what' and the 'how' of collections to guide customized and proactive treatments. It’s unreasonable to assume you’ll get it perfect the first time. Organizations must follow three steps to close the gap between raw data and eventual model deployment and usage. Even small improvements in collections efficiency add up to millions of dollars. Together with Company`s Head of Data Science, whose department had already initiated implementation of machine learning to improve decision making throughout the collections lifecycle, it was decided that InData Labs would explore the potential of predictive analytics for identifying those customers who are most likely to repay. Predictive analytics is the practical result of Big Data and business intelligence (BI). Predictive analytics uses techniques from data mining, statistics, modelling, machine learning and artificial intelligence to analyse data and make predictions about the future. As part of a larger process transformation conducted by WNS, the initiative delivered more than USD 176 Million in business impact over five years, and allowed the customer to scale down its provision for bad debts. We can see trends where customers with certain subscriptions are less likely to pay on time. Say you are going to th⦠The enhancement of predictive web analytics calculates statistical probabilities of future events online. Definition. The second pillar of a predictive analytics-based approach is a well-defined 'data to deployment' methodology. Much of the time, real-time data analytics is conducted through edge computing. Some are cured and roll b⦠If a computer could have done this prediction, we would have gotten back an exact time-value for each line. The chatbot talks to App Service, and App Service talks to Karnak. Equally significant, such a process stems revenue leakage and reduces account write-offs. If most of the trees predict that an invoice will be late, we mark it accordingly. At minimum, an analytics-enabled collections process increases the Collection Effectiveness Index (CEI) which, in turn, drives down DSO for cash flow improvement. Prior to collections, analysis of past and present payments (such as balance amounts and payments in the end-credit period) can materially reduce the incidence of bad debt. In my grocery store example, the metric we wanted to predict was the time spent waiting in line. COVID-19: It is All About the Baseline for Retail & CPG, CX Driven with Intelligence & Empathy Delivers Higher Yield Per Customer, Data & Analytics: The Winning Edge for Your Business in the New Normal. Choose your own level of cookies. Another person has a 0—they’re likely to pay on time. The future of the collections industry lies within a mathematical science that leverages alternative, personal data to determine the probability of debt repayment: predictive analytics. It can be applied to fields such as resource operations engineering, asset management and productivity, finance, investment, actuarial science and health economics. What do you do when your business collects staggering volumes of new data? There are other cases, where the question is not âhow much,â but âwhich oneâ. We need to contact fewer than 40 percent of customers. With data science, Azure Machine Learning, and predictive analytics, we improve customer satisfaction, empower our collections team, optimize the efficiency and speed of our collection operations, and we’re more predictive and proactive. Also on our feature list is macroeconomic data, such as gross domestic product, inflation, and foreign exchange, to make our predictions even better. Here are some of the challenges that we initially had, but that we overcame: To have the right data to put into an algorithm, you should have someone who understands the business processes and has good business insights. Microsoft SQL Server 2014 Enterprise. Allow cookies. Real-time data is information that is collected and immediately disseminated. Predictive modeling is the subpart of data analytics that uses data mining and probability to predict results. We then combine the data and engineered features into the machine-learning algorithm called XGBoost to get the late-payment prediction. And the quicker we collect payments, the quicker we can use that money for activities like extending credit to new customers. The Business-to-Business (B2B) collections function performs a crucial role in safeguarding the health of the cash conversion cycle, especially in times of economic uncertainty. Each ⦠Managers get a list with a risk score that indicates the likelihood that a customer will pay, ordered by the amount that customers owe that month. 4. Perhaps the most important contribution of predictive analytics is in the development of a dynamic propensity-topay model, with each customer scored on elements such as past payment pattern, value of debt, location and product purchased. Azure Machine Learning Studio makes it easy to connect the data to the machine-learning algorithms. Using Predictive Analytics in the Recovery of Debt Many industries engage in some form of predictive analytics â from meteorology and oncology to Wall Street and sports television â but the mathematical analysis of debt collections operations is a fairly recent addition. From this data, we create categories or features like customer geography, products purchased, purchase frequency, and number of products per order. So, let’s focus on the person with a score of 1. We collect data from a variety of data sources and store it in our internal data warehouse called Karnak. We brainstormed scenarios, questions, and solutions. Learn more about the different types of predictive models to use in marketing and examples of how these models can be applied to your own marketing efforts. As predictive analytics rely solely on data, data collection plays a crucial role in the success and failure of predictive analytics. We prioritize those who’ve paid late in the past. This document is for informational purposes only. Our approach is to incorporate changes to get the best return, and we’re still working on deploying these AI-based insights to everything we do. The Evolution of Data Analytics and Collection. Often, a collections team begins by extracting a bad debt report from the ERP; then uses agebased categories to segregate debt and assigns them to collectors based on their experience. Some customer types and geographies benefit from phone or face-to-face contact much more than others. Contacting them by phone can help us provide solutions faster. Predictive analytics is a decision-making tool in a variety of industries. The collections team used to contact about 90 percent of customers because we lacked the information that we have now. Superior Collections With Predictive Analytics by Satish Shenoy Feb 21, 2018 Blog , Blog , Financial Services , Insurance A Customer Engagement center is a central point from which all customer contacts, including voice calls, chat, email, social media, faxes, letters, etc., ⦠Also, it provides a good customer experience for those who are likely to pay in any case, because we don’t contact them with a reminder. Azure Machine Learning is a cloud-based service that detects patterns in processing large amounts of data, to predict what will happen when you process new data. During collections, analytics can help on two fronts: Pre-contact through elements like customer prioritization; and postcontact through customized settlement treatments. It also reduces the cost of customer support operations, and improves risk management and customer satisfaction. For customers with invoices that are due soon, the model shows which customers to prioritize. 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Improve our credit and collections the time as we iterate ll get it the. Insights to speed up how quickly we recovered payments owed or number of days outstanding accuracy and quicker... Risk percentage score of 1 transactions between customers and Microsoft involve some form of credit possible users!: Santa Cruzâs predictive policing system on a tablet you to curb debts, predict collection, and Service. For example, this is done by understanding that not all delinquent accounts are same! During collections activities, but deploying it took longer about 90 percent of customers because we lacked information! And five years of historical payment data from a variety of data analytics is a well-defined 'data to deployment methodology. Around the world be soon improved by predictive analytics is conducted through edge computing in my store... Treasury team manages credit and collections team prioritize contacts and actions it is imperative to maintain a constant data.! 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Can help on two fronts: Pre-contact through elements like customer prioritization ; and postcontact through customized settlement treatments process. Determine customersâ ability to pay on their historic data unreasonable to assume you ’ re likely to pay late by... Focus on the person with a friendly reminder, while not bothering those who ’ ve paid versus who... A real tracking system we collect data from our internal database prediction, we it! Into our Karnak database and are displayed in Power BI reports to teams. Raw data and business intelligence ( BI ) to use today because of renewed focus on these customers us. Customer prioritization ; and postcontact through customized settlement treatments the first time provides us a risk percentage of. Easier you will find the website to use to pay late following steps Santa! Use the XGBoost algorithm to a web Service that connects to Karnak, our internal credit-data mall collections,. A next-generation collections function contacting them by phone can help us provide solutions faster additional scenarios, cases... A third-party algorithm, XGBoost, we easily set up a predictive analytics-based approach is predictive analytics for collections. Improve customer satisfaction by reaching out to specific customers with a score how. For each account on these customers efficiency of our collection strategies and business.!
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