Integrating Credit Decisions with the Back Office

Experian is a CMA credit reporting partner.
Back office credit decisioning and why it matters

By Carl Stronach, Experian

When you’re launching a new product, business line, or starting up a business, you’ve got to move fast and break things.  This means taking a minimum viable product (MVP) approach, where you’ve got to sacrifice scalability by implementing manual processes to support the early stage business.  Commonly, a manual process will be in place for credit applications and approvals – pulling the credit report, reviewing the data against a scorecard or policy and then making the decision. Since this likely takes a day — or often longer — the process decreases your customer’s experience, and can hurt your ability to scale and grow revenue the longer you wait to automate.

To grow the business and take it to the next level, you need to migrate away from the paper-pushing approach. The next step is to move toward an automated solution that integrates credit decisions with the back office, such as an ERP, CRM, or other custom system, employing APIs.

Using an Application Programming Interface (API) to Connect to Your Decision Engine

An API, or Application Programming Interface, is many things. It’s a set of instructions and technical documentation for developers. It’s a collection of services which allow you to interact with a product or service. And it’s a way for businesses to open-up and allow for new kinds of innovation – allowing for new business models and application development that wouldn’t be possible without APIs.

In the last decade, APIs have become system agnostic, meaning they plug-and-play into nearly any system because they are standardized and popular amongst the development community.

Because of this popularity, APIs make it easier for the business to get buy-in from the IT department, which is essential to automating the credit decisioning process. Without an API, the IT department must devote significant resources to the project because more infrastructure to host large database will be required. APIs allow you to pull data in real-time only when you need it, reducing system complexity and decreasing application development costs.  Reduced complexity also means less risk because you are more assured that your IT department will be successful with the integration. Often, when IT departments are presented with information about the API, their response is “No problem, this is standard. We have integrated with a very similar API before. We can do this.”

How does your decision engine interact with APIs? You can use APIs to get the raw data elements your credit policy or model needs to render a decision, no matter if the data is internal to your business or provided by third parties.

Taking Decisions to the Next Level with Machine Learning

According to a recent Harvard Business Review project, the key to successfully utilizing machine learning isn’t to get caught up in new and exotic algorithms, but to make the deployment of machine learning easier.  There are many use cases where machine learning can be employed, but use cases where data-driven decisions are being made, as in the credit approval process, are archetypical.

During the early stages of the machine learning process, you train the model by feeding it data from past applications. Then, as you use the engine for real-time processing, the engine learns from past decisions. If the engine was originally approving applications with a borderline credit score, but found that these applications often ended up being poor risks, the model would then begin turning down these applications.

The key ingredient in making machine learning start to work for your credit department is to have domain experts, credit managers, help the IT department focus on the key variables that can help the machine learning model to predict key outcomes – credit losses, bankruptcies, and business failures, and to put the models through many rounds of testing and validation before putting them into real-life practice.

Now is the time to move your manual processes online using an API and machine learning. According to Mary Meeker’s Annual Internet Trend Report, 60 percent of customers pay digitally compared to 40 percent in the store.  And it’s likely that the gap will continue to grow. The longer you wait, the further ahead your competitors will be in digitizing the customer experience — and the harder it will be to regain your footing and catch up.

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 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:

Analytical_sandbox


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 A/R Analytics Matter, by Mark Wilson

In today’s business world, virtually every department in every company uses analytics to create efficiencies, make better decisions, and improve results. For example, a sales manager is no longer basing the sales team’s success solely on the number of sales made—that person is utilizing technology that looks at a number of metrics beyond those final sales. Why? For multiple reasons. Analytics can provide any business area with information that helps them stay competitive, streamline processes, hold their team accountable, and keep their customers satisfied.

Credit and collections teams should be utilizing this same type of technology to track metrics relating to their business area, however two obstacles often arise: (1) many credit professionals don’t know this technology even exists, and (2) those that do know it exists assume that implementation is an expensive and grueling process that requires a lot of IT support.

So, what if you had an easy way to go beyond DSO and track metrics that will transform your company’s A/R into a strategic and value-driven operation? Would you be interested? What metrics would matter? What if there was a free trial of a software that would let you do this so that you could decide if this was a valuable tool?

My company, TermSync, offers a cost-effective accounts receivable software, that is easy to integrate and use. By offering a program exclusive to CMA members, we’d like to make it even easier for you to track important AR metrics. CMA members can use TermSync for FREE until the end of 2015 if you sign up before September 30.

As a Preferred Partner with NACM National, the TermSync team has come to realize how innovative NACM members are and how much they really care about improving their credit and collection processes, especially those in the CMA chapter.

If you’re interested in learning more, join our free webinar on Thursday, September 10, at 9am PST surrounding The 6 Metrics You Should Be Tracking to Guarantee Success. Register here!

Mark Wilson, a former CFO, is the President of TermSync, a cloud-based accounts receivable software company owned by Esker, Inc. Mark will present this topic at his webinar on September 10. Register here.

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.