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BETTERING DEBT COLLECTION WITH PREDICTIVE MODELS

By Tim Stone posted Tue January 26, 2021 10:26 AM

  

Consumer Financial Protection Bureau mentioned that, Americans recorded more complaints about debt collections than about any other financial episode. Of the 310,710 complaints accepted by the CFPB about debt stock in 2017, the most popular was, “Continued efforts to collect debt not owed,” which was mentioned by 39 percent of case filers.

Debt collection in finance is beginning to be disrupted by AI due to the availability of large amounts of historical records of clients for banks and other financial companies. Most AI uses that have real-world business importance for debt collection now seem to be in personalizing connections to customers and recognizing clusters of similar debtor outlines.

Badly assessed financial dangers were at the core of the financial pressure in the late 2000s. Banks and credit associations used faulty patterns which did not highlight the actual threat of the mortgages granted. When the house bubble burst, it managed to the collapse too big to fail financial companies and the entire economy's recession for a few years. All these difficulties could have been withdrawn with proper risk hedging instruments. 

Think if a piece of software could show you the repayment promise both for current, but also for coming customers. Of course, that is what the FICO scoring standard aims to do, but it has not been completely successful as we've seen.FICO is for Financial Accounting and Controlling. Rules based on imminent analytics which use big data could have a greater chance of predicting repayment opportunities. Yet, most businesses have not used these tools yet.

Let’s go through a brief display of the importance for predictive analytics in the сommercial сollections industry.

Danger assessment: Customer scoring

As discussed before, for the late 80s the FICO score has been the golden rule for evaluating loan request and creditworthiness. It even appears in a few different characters, including auto FICO, and healthcare FICO. 

Machine learning and especially predictive analysis can take this method beyond a simple number and build a 360-degree representation of the client, considering more than just the credit records and current debts. Now, it can combine data from social media, using patterns and more. 

Such an instrument would be great for international clients who have no prior FICO score but would be great business companions, like foreign investors. It would also offer a good opportunity to recent college grads or other young people.

By considering a broader array of input information, the truth of the prediction updates consistently, and it can also be refined to a very individual level. The new issues can go as far as placing an individual credit score limit to reduce potential damage. 

COMPUTE PAY OR DEFAULT PROPENSITY 

Practicing survival patterns, each client account can be assessed for its likeliness to become a possible loss. If an account is in a constant downward trend about its motivation or ability to handle it should be treated as a possible risk before it becomes one.

Predictive analysis rules can determine the payment patterns which show that a client is struggling. For instance, it could begin with being just a few days late or spending two sections at once. Any difference from the usual fee schedule should be a red flag for the system. 

A tool could be put in place which self-triggers when an undesired pattern emerges. The way could reach out to the customer and ask them if they require help if they are going through a hard financial moment to offer answers before building up debt. 

Prediction cash flow

Any company wants to know what they can expect about future cash flows. Financial companies are no different, and predictive investigation can support them in making more accurate projections for scheduled receivables. 

Since the model was able to recognize clients who can be late or default altogether in the risk evaluation part, it follows that it is foreseeable to say which clients will pay. 

Debt collector’s market models depend on predicting the success of collection operations and assessing outcomes at the end of each month even before the billing cycle starts.

This supports redirect the workforce from concentrating on clients who were going to pay anyway towards those who are most likely not to meet their responsibilities. 

Become the client relationship 

Not only can predictive investigation tell you which customers are the highest risks for your company, but it can also know when it’s best to get in touch with them for best results. For instance, if they work in groups, it’s best to call them whey they are not at work or resting, which could be outside regular working hours. 

Explaining your clients that you care about their rules and lifestyle improves your odds of them listening to your call agents and eventually raising the group rates. Of course, to train the way, you need the logs of past discussions.

PREDICT CALL VALUE 

As in any market, debt collectors aim to maximize their ROI by letting the call agents concentrate on the most promising accounts instead of just following a list approach. By using predictive analytics as described before, the model can designate a likelihood score to each possible call and rank them in the company’s CRM.

The model normally works by computing the payment probability, taking as a modifier the event “call”. That suggests, in fact, calculating the probability when the client is not called and the chance when it receives a call. This is a simplistic way, yet it highlights how such rules could make a variation for the bottom-line. 

Thinkable challenges

As in all models compared to big data, the main problem is related to data cleaning. Since it’s a matter of waste in, waste out, before making any prediction, the organization dealing with this task requires first building the pipeline to bring in the date, rinse it and use it to train the neural system.

Another difficulty might be related to different personal data security regulations and privacy concerns. 

READY, SET, PREDICT

To fix it all together, it’s worth considering that predictive models can make a variation when it comes to income for debt collection agencies. It can boost growth rates by targeting the right forms at the right time. Of course, an in-depth investigation could go about recognizing the message that would change the client the most. 

Targeting the correct clients also means more potency for call centre agents and fewer lost work hours. Having a scientific model behind the decisions reduces bias and makes the


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