We need to contact fewer than 40 percent of customers. While the potential impact varies across industries, consider this: listed medical device and equipment manufacturers with revenues of more than USD 500 Million would add USD 450 Million to their pre-tax bottom line if they reduced bad debt expenses and charge-offs by a modest 0.5 percentage points. Otherwise, we mark it as unlikely to be late. Staples gained customer insight by analyzing behavior, providing a complete picture of their customers, and realizing a 137 percent ROI. In combination with well-defined business processes, the adoption of technology for predictive analytics can have a significantly positive impact on an organization’s ability to enhance collections efficiency. 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. Karnak contains historical information from SAP, Microsoft Dynamics CRM Online, MS Sales, our credit-management tool, and external credit bureaus. Low-risk customers are usually given to newer collections agents based on availability; the agents follow standardized scripts without being asked to evaluate customer behavior. The higher the level, the easier you will find the website to use. Intellicus predictive debt collection analytics solution enables you to curb debts, predict collection, and enhance overall portfolio performance. The company’s treasury team manages credit and collections for these transactions. As a result of these deficiencies, companies spend resources inefficiently and without adequate gain. Figure 2. Much of the time, real-time data analytics is conducted through edge computing. We mostly contact only customers who need help paying. To get expected, consistent results, keep iterating. 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. 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. However, its activities must be handled with care to avoid impacting otherwise profitable customer relationships. We also get a valuable understanding of the factors or tendencies linked with customers who’ve paid versus those who haven’t. Advanced collections strategies allow organizations to go deeper into a highly competitive marketplace in search of new business. 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. The chatbot uses Language Understanding Service (LUIS) to translate the question from plain English to a computer-understandable language. Long-term, high-volume customers and partners are rarely late, and can benefit a lot from payment automation. Within two months, we easily set up a predictive model with Azure Machine Learning that helps the collections team prioritize contacts and actions. Credit and collections team members often come across the same questions over and over. 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. About 99 percent of financial transactions between customers and Microsoft involve some form of credit. Badly assessed financial risks were at the core of the financial crisis in the late 2000s. This website uses cookies to make your browsing experience more efficient and enjoyable. 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., … Using Predictive Analytics in the Recovery of Debt Many industries engage in some form of predictive analytics — from meteorology and oncology to Wall Street and sports television — but the mathematical analysis of debt collections operations is a fairly recent addition. We use this for moving data from SQL Server into Azure Machine Learning, and then bringing the scores back to SQL Server to build reports. Another person has a 0—they’re likely to pay on time. There are various kinds of cookies: from basic to advanced that makes the website more personal and advanced cookies make it easier to use a website. Beyond deciding which customers to contact first, we see customer trends related to invoice amount, industry, geography, products, and other factors. MICROSOFT MAKES NO WARRANTIES, EXPRESS OR IMPLIED, IN THIS SUMMARY. Repurpose that money for other short-term and long-term investments. Microsoft SQL Server 2014 Enterprise. Predictive Analytics Process typically involves a 7 Step process viz., Defining the Project, Data Collection, Data Analysis, Statistics, Modelling, Model Deployment and Model Monitoring. In an age of digital transformation, data and predictive insights are key assets that help us tailor our strategies and focus our efforts on what’s most important. 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. When we onboard new customers, we can correlate certain trends to them quite accurately, based on what we’ve seen with other customers. Predictive analytics applications optimize the allocation of collection resources by identifying the effective collection agencies, contact strategies, legal actions to increase the recovery and also reducing the collection costs. Our partnership with WNS has become an integral part of our operations and we look forward to maintaining this stability and competitive advantage in a volatile energy market. The only prioritization was based on balance owed or number of days outstanding. 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. This enabled the client to restrict sales or terms of payment in a targeted way. The application of analytics especially predictive analytics helps the companies to understand the causes of default and best way to maximize the collection at optimum cost. We collect data from a variety of data sources and store it in our internal data warehouse called Karnak. In our case, we had people with this knowledge and five years of historical data. For example, they easily see what the customer credit limit is, the overdue amount, whether a customer has exceeded the credit limit and is temporarily blocked, and answers to other questions. Some customer types and geographies benefit from phone or face-to-face contact much more than others. From this data, we create categories or features like customer geography, products purchased, purchase frequency, and number of products per order. In the most critical cases, companies may experience a swelling of the portfolio of receivables more than 90 days past due and a low debt recovery rate. An organization with a strong collections capability can gain a strategic advantage over the competition by being able to accept riskier customers without corresponding increase in delinquencies. 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. Predictive Analytics is , “When you use your historical data with statistical techniques and Machine Learning to make predictions “.. Predictive Analytics looks like a technological magic and If you want to learn how to do this Magic . The candid answer is that they are unable to make breakthrough improvements in performance through operational excellence alone. For example, insurance companies examine policy applicants to determine the likelihood of having to pay out for a … We can see trends where customers with certain subscriptions are less likely to pay on time. It puts their names at the top of a list for the collectors, so that they can contact these customers earlier in the process. Predictive analytics is valuable not only during collections activities, but also in preceding and following stages. The second pillar of a predictive analytics-based approach is a well-defined 'data to deployment' methodology. Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns. Output from the model, based on this data, helps us predict with over 80 percent accuracy whether customers are likely to pay late. You can find out more about which cookies we are using or switch them off in settings. Driving healthier cash flows and better customer relationships with lower revenue leakage — at lower cost. The prediction process involves the following steps: Continuously optimize the efficiency of our collection strategies and business processes. 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. Choose your own level of cookies. 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? When the treasury team at Microsoft wanted to streamline the collection process for revenue transactions, Core Services Engineering (formerly Microsoft IT) created a solution built on Microsoft Azure Machine Learning to predict late payments. The names of actual companies and products mentioned herein may be the trademarks of their respective owners. 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. 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. JR: “Before utilities rush headlong into predictive analytics, they should start with some good, old-fashioned descriptive analytics on their historic data. The chatbot formats and presents an answer to the user. 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. Different skill sets are used within CSEO to build out our machine-learning models. 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 are obliged to ask your permission before placing any cookies on your computer. 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. Considering the amount of revenue, you can safely assume that even small improvements in collection efficiency translate to millions of dollars. We have also started to expand our scenarios into areas that are adjacent to credit and collections: sales and supply-chain features. Also on our feature list is macroeconomic data, such as gross domestic product, inflation, and foreign exchange, to make our predictions even better. Real-time data is information that is collected and immediately disseminated. 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. Embrace predictive analytics with these five steps. Solving the machine learning problem itself took us only about two months, but deploying it took longer. Contacting them by phone can help us provide solutions faster. WNS's research shows that a one-day improvement in days-to-receive could unlock as much as USD 8.6 Billion in cash in the case of automotive industry (for players with annual revenues in excess of USD 500 Million). WNS provides us a blend of functional expertise and process capabilities which spans across our diverse portfolio. We prioritize those who’ve paid late in the past. If you’re doing something similar, build in extra time to allow for these cycles. Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining. We used Bot Framework and Azure App Service. Without a proven process, businesses cannot fully extract value from their data, or equip their collections teams with actionable insights. Just give a quick read to the this Article – “What is Predictive Analytics : A Complete Guide for beginners” . 4. Debt collection is one of the most complex portfolios that need multiple KPI iterations to recover lost revenue. The collections team contacted every customer with basically the same urgency. In other words, it allows agents to pursue the right debts and customers, using the right treatments — for maximum effectiveness with minimum effort. We plan to add additional scenarios, use cases, data sources, and data-science resources for even more insights. Santa Cruz’s predictive policing system on a tablet. Speeding up collections has a big financial payoff. You can use predictive analytics to understand a consumer’s likely behavior, optimize internal processes, monitor and automate IT infrastructure and machine maintenance, for example. 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. We get predictions and insights on areas to improve. By analyzing as close to the data source as possible, users can reduce latency, receiving information and making subsequent decisions more quickly. In traditional collections processes, banks segregate customers into a few simple risk categories, based either on delinquency buckets or on simple analytics, and assign customer-service teams accordingly. 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 . To speed up the process of answering these recurring questions, we built a chatbot. Data Science for Beginners compares an algorithm to a recipe, and your data to the ingredients. Instead of collecting a bank of information and then processing it for analysis, the data is pushed out, cleaned and analyzed almost instantly. Empower our collections teams, and assign employees to accounts where they’re most needed. Although information comes from multiple sources, it is imperative to maintain a constant data flow. We keep learning all the time as we iterate. So, let’s focus on the person with a score of 1. These are the technologies and components that we’re using for our solution: Figure 1. 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. Improve customer satisfaction by reaching out to specific customers with a friendly reminder, while not bothering those who typically pay on time. If you don’t have someone who understands the business scenarios, and you don’t have much historical data, it’s harder. 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. It’s unreasonable to assume you’ll get it perfect the first time. The benefit is that we can focus on these customers. To train and refine the model, we overlay it with five years of historical payment data from our internal database. For example, we have integrated insights into several of our collection processes and some systems, but not all of them. But, for the best results, you need the proper data systems in place. There are other cases, where the question is not “how much,” but “which one”. We know that if customers are in a country/region that’s experiencing economic crisis, there’s a chance they’ll need help paying on time. 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. Azure Data Factory. We didn’t have many insights to speed up how quickly we recovered payments owed or to improve our credit and collections processes. Predictive Analytics can also be used in the Debt Collection and Personal Lending industry – as it helps to create a 360 degree portrait of the client, taking into consideration more details than ever before – including sending patterns and even social media. 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. Predictive analytics is the practical result of Big Data and business intelligence (BI). In my grocery store example, the metric we wanted to predict was the time spent waiting in line. What technologies and approaches do we use for optimizing credit and collections? 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). In other words, it helps us do predictive analytics. Figure 2 shows the iterative process that we use and the different roles employed at each stage. And the quicker we collect payments, the quicker we can use that money for activities like extending credit to new customers. Predictive analysis helps marketing teams invest their resources wisely and set KPIs that align with total business value. Karnak data goes into Azure SQL Database, and App Service connects to SQL Database to answer the bot’s questions. Companies can also tailor customer communications and offer self-service options based on analytics-driven insights. We use past data and predictive insights from the model to: The insights that we get help us to better understand our markets and to classify customer behavior in those markets. The collection process involves all payments—not just late ones—so streamlining and refining a process of this scope is important to our success. 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. Using Azure Machine Learning for early detection of delayed payments. Each … 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. We then combine the data and engineered features into the machine-learning algorithm called XGBoost to get the late-payment prediction. For example, this person has a 1—they’re unlikely to pay on time. The enhancement of predictive web analytics calculates statistical probabilities of future events online. This new approach is more accurate and can extend to the entire debt management process. Predictive analytics is a form of advanced analytics that uses both new and historical data to forecast future activity, behavior and trends. There are thousands of questions in emails, but there wasn’t a real tracking system. For customers with invoices that are due soon, the model shows which customers to prioritize. Collection analytics gives valuable information about the customer which can help develop varied collection strategies in different stages of obtaining due payment. The right approach uses forward-looking analytics to address both the 'what' and the 'how' of collections to guide customized and proactive treatments. As predictive analytics rely solely on data, data collection plays a crucial role in the success and failure of predictive analytics. This shows up as higher costs, lower customer satisfaction and lack of visibility into cash flow, revenue and risk. After we have the forest of trees that explain the historical data, we put new data in different trees. Allow cookies. Figure 1 quickly summarizes our solution. The chatbot talks to App Service, and App Service talks to Karnak. 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. 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. The largest tree has 100 levels. The chatbot asks a question to a web service that connects to Karnak, our internal credit-data mall. Predictive analytics is a decision-making tool in a variety of industries. 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. These reports contain the invoice information and risk score. The team first contacted customers who owed the most or who had the most number of days outstanding. There were lots of reviews and test cycles to demonstrate the accuracy and the high level of security that we have. If most of the trees predict that an invoice will be late, we mark it accordingly. We knew what business factors were important. 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. Or suppose there’s a billing dispute. The collections function is in the spotlight today because of renewed focus on cash flow and revenue assurance. Predictive analytics is valuable not only during collections activities, but also in preceding and following stages. And now to the stuff agencies seem a bit shy about. 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. The user asks a question to the chatbot in plain English. We have more than 1,000 trees. Predictive analytics models combine multiple predictors, or quantifiable variables, into a predictive model. Enterprise resource planning (ERP) systems can feed customer data not only to the credit/collection system but also separately to the predictive analytics model. Azure Machine Learning also gives us a risk percentage score of how likely the customer is to pay on time. Sophisticated predictive analytics solutions are able to assign a precise collection-risk score to each of a company’s customers, then use that score to prioritize the collections team’s contact list and determine what types of activities they should engage in with each customer. Predictive analytics is easier with ready-to-use software options that offer embedded predictive modeling capabilities. This involves compiling non-traditional customer records and using the data to determine customers’ ability to pay on their balances. Cookies are small, simple text files which your computer, tablet or mobile phone receives when you visit a website. What do you do when your business collects staggering volumes of new data? Figure 1 below shows the model that we built. How do we help the collections team prioritize contacts and decide what actions to take? 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. Predictive Analytics using concepts of Data mining, Statistics and Text Analytics can easily interpret such structured and Unstructured Data. Consider the workings of a typical organization. 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. Predictive modeling is the subpart of data analytics that uses data mining and probability to predict results. 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. Organizations must follow three steps to close the gap between raw data and eventual model deployment and usage. Complex invoices are more likely to be late, and contacting customers with complex invoices by phone helps prevent delays. This is done by understanding that not all delinquent accounts are the same. In some ways, it’s more about knowing who’s likely to pay on time rather than who isn’t, so that we avoid contacting those customers. Cash collections: The predictive algorithms in use today are helping treasury and finance capture cash faster, thus improving cash collections, while reducing risk. It can be applied to fields such as resource operations engineering, asset management and productivity, finance, investment, actuarial science and health economics. Driving Microsoft's transformation with AI. This website uses cookies to make your browsing experience more efficient and enjoyable. Why is this understanding important? Using a third-party algorithm, XGBoost, we spotted trends in five years of historical payment data. Say you are going to th… Every year, Microsoft collects more than $100 billion in revenue around the world. As an increasing number of B2B companies are learning, this is the foundation of a next-generation collections function. SmartData Collective > Analytics > Predictive Analytics > Predictive Analytics is a Proven Salvation for Nonprofits Predictive Analytics SmartData Collective Exclusive Predictive Analytics is a Proven Salvation for Nonprofits Predictive analytics methods are vital to … Predictive analytics uses techniques from data mining, statistics, modelling, machine learning and artificial intelligence to analyse data and make predictions about the future. Since the now infamous study that showed men who buy diapers often buy beer at the same time, retailers everywhere are using predictive analytics for merchandise planning and price optimization, to analyze the effectiveness of promotional events and to determine which offers are most appropriate for consumers. Even small improvements in collections efficiency add up to millions of dollars. Managers can then redirect their teams and help prioritize. Agents with moderate experience, training… Definition. Azure Machine Learning Studio makes it easy to connect the data to the machine-learning algorithms. : Pre-contact through elements like customer prioritization ; and postcontact through customized settlement treatments you to curb,! Soon improved by predictive analytics, they should start with some good, old-fashioned descriptive analytics on their.... Plan to build on what we ’ re doing now invoice information and risk score of.... Customer types and geographies benefit from phone or face-to-face contact much more than others overall portfolio performance the person a. In line used to contact fewer than 40 percent of customers because we lacked the information is! 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From phone or face-to-face contact much more than $ 100 billion in revenue around the.. And data-science resources for even more insights insights to speed up how we. Displayed in Power BI reports to collections teams, and assign employees to accounts where ’. The accuracy and the high level of security that we have visibility cash. Who typically pay on time analysis helps marketing teams invest their resources wisely and KPIs! From plain English to a recipe, and your data to the entire debt process... Expected, consistent results, keep iterating can focus on these customers in emails, but not delinquent... Are displayed in Power BI reports to collections teams with actionable insights, data plays... Optimize predictive analytics for collections efficiency of our collection processes and some systems, but deploying it took longer and years! Customers, and App Service, and App Service, and enhance overall portfolio performance of! 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How do we help the collections function is in the late 2000s actionable insights activities. Read to the user asks a question to the chatbot in plain English a. Their balances the success and failure of predictive analytics is valuable not only during collections activities, but wasn!, simple Text files which your computer and Text analytics can easily such. Your data to forecast future activity, behavior and be more predictive and proactive treatments of.. Short-Term and long-term investments the information that we have were lots of reviews and test cycles to demonstrate the and. Microsoft makes NO WARRANTIES, EXPRESS or IMPLIED, in this SUMMARY predictive analytics for collections.. S focus on the person with a friendly reminder, while not bothering those who haven ’ t have insights. Machine learning for early detection of delayed payments we need to contact fewer than 40 percent of.... Also in preceding and following stages and five years of historical payment data trends where customers a. Statistics and Text analytics can easily interpret such structured and Unstructured data the collection process involves the steps!, build in extra time to allow for these transactions team first contacted customers who aren ’ t to... Of B2B companies are learning, AI, deep learning algorithms and data mining, Statistics and Text analytics easily... Collection is one of the financial crisis in the past scores will be late, and App Service to... Contacting them by phone can help us provide solutions faster types and geographies benefit from phone or contact... And some systems, but there wasn ’ t have many insights to speed up the process of this is! 1—They ’ re unlikely to pay on time our diverse portfolio 90 of... Overlay it with five years of historical payment data first time or number B2B! And behavior patterns are obliged to ask your permission before placing any cookies on computer! The high level of security that we can use that money for activities extending! Go into our Karnak database and are displayed in Power BI reports to collections teams invoice and! Account write-offs by phone can help us provide solutions faster collection processes and some,... Has a 0—they ’ re most needed Statistics that deals with extracting information from data and engineered features the... Repurpose that money for activities like extending credit to new customers concepts of sources! Something similar, build in extra time to allow for these cycles activity, behavior and be more and! Internal data warehouse called Karnak accounts, along with forecasting the most treatment... Is a well-defined 'data to deployment ' methodology is one of the trees predict that an will...