Why Financial Institutions Need an Analytics Sandbox

Experian is a CMA credit reporting partner.

The appetite for businesses incorporating big data is growing significantly as the data universe continues to expand at an astronomical rate. In fact, according to a recent Accenture study, 79% of enterprise executives agree that companies that do not embrace big data will lose their competitive position and could face extinction. Especially for financial institutions who capture and consume an incredible amount of data, the challenge becomes how to make sense of it. How can banks, credit unions, and other lenders use data to innovate? To gain a competitive advantage?

This is where analytics sandboxes come in.

A sandbox is an innovation playground and every data-consuming organizations’ dream come true. More specifically, it’s a platform where you can easily access and manipulate data, and build predictive models for all kinds of micro and macro-level scenarios. This sounds great, right? Unfortunately, even with the amount of data that surrounds financial services organizations, a surprising number of them aren’t playing in the sandbox today, but they need to be. Here’s why:

Infinite actionable insights at your fingertips
One of the main reasons lenders need a sandbox environment is because it allows you to analyze and model many decisioning scenarios simultaneously. Analysts can build multiple predictive models that address different aspects of business operations and conduct research and development projects to find answers that drive informed decisions for each case. It’s not uncommon to see a financial services organization use the sandbox to simultaneously:

  • Analyze borrowing trends by type of business to develop prospecting strategies
  • Perform wallet-share and competitive insight analyses to benchmark their position against the market
  • Validate business credit scores to improve risk mitigation strategies
  • Evaluate the propensity to repay and recover when designing collection strategies

A sandbox eliminates the need to wait on internal prioritization and funding to dictate which projects to focus on and when. It also enables businesses to stay nimble and run ad-hoc analyses on the fly to support immediate decisions.

Speed to decision
Data and the rapid pace of innovation makes it possible for nimble companies to make fast, accurate decisions. For organizations that struggle with slow decision-making and speed to market, an analytics sandbox can be a game changer. With all your data sources integrated and accessible via a single point, you won’t need to spend hours trying to break down the data silos for every project. In fact, when compared to the traditional archive data pull, a sandbox can help you get from business problem identification to strategy implementation up to 30% faster, as seen with Experian’s Analytical Sandbox:

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Cost effective analytics

Building your own internal data archive with effective business intelligence tools can be expensive, time-consuming and resource-intensive. This leaves many smaller financial services at a disadvantage; but sandboxes are not just for big companies with big budgets. An alternative solution that many are starting to explore is remotely hosted sandboxes. Without having to invest in internal infrastructure, this means fast, data-driven decisions with little to no disruption to normal business, fast onboarding, and no overhead to maintain.

For financial institutions capturing and consuming large amounts of data, having an analytical sandbox is a necessity. Not only can you build what you want, when you want to address all types of analyses, you’ll have the insights to support business decisions faster and cheaper too. They prove that effective and efficient problem solving IS possible!

CMA offers solutions, such as Experian and other bureau credit reports, Industry Credit Groups and more to help companies determine how much business credit to extend. For more information on how we can help your company, contact Credit Management Association at 818-972-5300 or visit www.CreditManagementAssociation.org.

This article originally appeared here and has been reprinted with permission.

Why Data Models Matter More Than Ever

By Brodie Oldham, Experian

Are the credit models you are using to make lending decisions more than 2 or 3 years old? If so, you are likely making less than optimal credit decisions. You may be turning down a customer who is a good risk — while taking on customers who are more apt to default on their obligations.

Every year a model isn’t updated, its accuracy decreases. The economy changes. The consumer’s or business’s financial situation changes. Updating your models, using the most current data and attributes available, you can have confidence that you are making good credit decisions.

To make the most accurate credit decisions possible, many businesses are now turning to data-driven decisioning models that are powered by artificial intelligence (AI) within machine learning engines. While the standard regression model works well in some industries, the lift in predictive value from using AI data models can be very important in other industries, such as retail, fraud and marketing. These models use sophisticated algorithms to predict the customer’s future ability to repay their obligation, which means a much more accurate decision than traditional models.

Starting with High Quality Data

Primary-Experian-Violet-data-iconWhile data has always been at the core of credit decisions, models using machine learning are even more dependent on data. These models can be very accurate, but their accuracy depends on having the necessary data to understand what happened in the past and present behavior to make a prediction for what will happen in the future. The more data provided, the higher the accuracy of the decision. Here are three things to consider when building your data-driven decisioning model:

 

Clean Data – As innovation spurs business and technology to run faster and more efficient, the quality of the data underneath all of that innovation becomes even more important. Machine learning becomes smarter the more data it consumes. This means the accuracy of the credit decisions made by the model is largely dependent on the quality of the data provided. Data from third-party sources often contains mistakes, missing fields, and duplicate information, which results in less accurate credit decisions.

Correct Data Points – The accuracy of the results depends on considering the right criteria in the form of data points in the model. When you use machine learning and AI algorithms, they can predict which specific data points will help increase the performance of the model for the specific customer and the specific type of credit decision. Often, data points that you may not consider are the ones that can make a big impact on the accuracy of the decision.

Real-Time Data – In the past, there was often significant lag time between collecting and being able to use the data. By using real-time data with machine learning models, you can get a clear picture of the most current view possible and see changes in the different data points as they occur. This lets you make a much more accurate prediction of what will happen, with the consumer or business, than was previously possible with a traditional credit decisioning process.

Using Alternative Data to Get the Full Picture

Primary-Experian-Violet-profile-iconOften, additional data — typically referred to as alternative data — that is not readily available from traditional data providers is used to enhance the accuracy and predictive ability of a model. While the model can seem complete without this information, the model may provide suboptimal results without it. Machine learning models can predict the situations and exact type of alternative data a model needs to produce an accurate decision. Experian offers a wide variety of alternative data that clients can use to improve decision models.

For example, a business owner may be taking out short-term loans to increase her cash flow, which makes her a much higher credit risk than she appears to be without this data. Weather information is also a common type of alternative data; a business located in Tornado Alley may need higher cash reserves to be a good credit risk. On the other hand, businesses located in an area impacted by a recent weather event, such as a hurricane, may be a good credit risk even with a lower score because both their business and local economy is recovering.

Regularly Evaluating Your Data Model

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You must build in governance and make sure you are evaluating how the model is working on a regular basis, like having an annual checkup with your healthcare providers. Once you begin using a data model, you can’t simply set it and forget it. Ask the following questions to periodically evaluate your models:

  • Are there changes in the outcome of the models? You need to verify that your attributes are still predicting the same outcomes as intended, as well as capturing the same data. For example, say you have an attribute in your model that counts the number of credit lines open for a small business. If the attribute changes and those types of credit lines are no longer reported by the data provider, that number can go from three or four to zero, without there being a change in the number of credit lines open by the business. Because the data that goes into your model has changed, your model is not accurate unless you update the attribute.
  • Is your model stable? You need to make sure that degradation hasn’t reached a point where the predictive value is no longer accurate. For example, scores before the 2008 recession have a different meaning than afterwards, due to the changes in the financial system.

The future of your business depends on making accurate credit decisions. Instead of using outdated models, use the latest technology and methods available by using machine learning data-driven models. It’s simple. It’s quick. And most importantly, data-driven models are accurate.

CMA offers solutions, such as Experian and other bureau credit reports, Industry Credit Groups and more to help companies determine how much business credit to extend. For more information on how we can help your company, contact Credit Management Association at 818-972-5300 or visit www.CreditManagementAssociation.org.

This article originally appeared here and has been reprinted with permission.

Using Predictive Analysis to Create Collection Management Strategies, by Christopher Rios

Traditionally, debt collection involved little more than picking up the phone and convincing the debtor why they need to pay for the products/services sooner rather than later. Today, credit and collection professionals are being asked to adopt more sophisticated techniques. One of the newer techniques utilizes predictive analytics to create collection management strategies. Predictive analytics permits creditors to identify at-risk A/R and focuses collection efforts on those customers with the greatest propensity for paying slow. This combination of historical AR data and predictive attributes will allow creditor companies to review and optimize their resource allocation, provide improved customer service, and to accelerate cash inflows. By doing so, creditor companies potentially reduce unnecessary costs across the credit to cash cycle and accelerate payments from high risk customers.

Inevitably, even the best collection strategies fall short at times. An organization’s fail-safe shouldn’t be to write off uncollectible receivables against its bad debt provision and move on. What is sometimes overlooked is the need for and the benefit of having a robust third party process for dealing with debtors that cannot or will not pay. Third party strategies should include bankruptcy administration, pre-litigation, litigation and mediation strategies.

Establishing a solid process that provides prescriptive treatment for dealing with non-paying, financially distressed customers will help creditor companies maximize the benefits of the third party services being provided. Ensuring you’re maximizing your return on investment and increasing the chances of recovering unpaid accounts receivable are two benefits of partnering with the right service provider.

This topic will be covered at the upcoming CreditScape Fall Summit, September 17-18 at the Tropicana in Las Vegas as I lead the discussion on creating a robust third-party collections process. For more information about the conference, visit www.creditscapeconference.com. I hope to see you there.

Christopher Rios is the Group Leader – Finance Operations for Dun & Bradstreet. He will be speaking at the CreditScape Fall Summit, September 17-18, at the Tropicana in Las Vegas.