Improvement Of Dept Collection With Predictive Models

Introduction

Poorly assessed financial risks were at the heart of the financial crisis in the late 2000s. Banks and lending companies used flawed models that did not highlight the real threat of mortgages. When the real estate bubble burst, it caused a very large collapse to break financial institutions and the recession of the entire economy for a few years. All of these problems could have been avoided with the proper hedging tools.

Imagine if the software could tell you the likelihood of repayment for current and future customers. Of course, this is what the FICO qualification model wants to do, but, as we have seen, it has not been completely successful. FICO stands for FI (financial accounting) and CO (control). Models based on predictive analytics that use big data may have a better chance of predicting payment possibilities. However, most companies have not yet adopted these tools.

Let's take a brief introduction to the applications for predictive analytics in the debt collection business.

Risk Assessment: Customer Score

As mentioned above, since the late 1980s, FICO scores have been the gold standard for evaluating loan applications and creditworthiness. It even comes in some different flavours, including FICO auto health care and FICO.

Machine learning and specifically predictive analytics can take this process beyond a simple number and create a 360-degree portrait of the customer, taking into account more than just credit history and current debts. You can now include social networking data, spending patterns, and more.

This tool would be ideal for foreign clients who do not have a previous FICO score, but who would be excellent business partners, such as foreign investors. It would also provide a fair opportunity for recent graduates or other young people.

By taking into account a wide range of input data, the accuracy of the forecast is constantly improving and can also be refined to a very personal level. The new results can go as far as setting an individual credit score limit to minimize possible damages.

Computer Payment or Defective Property

Using survival models, each client's account can be assessed as to the probability of becoming a potential loss. If an account is in continuous downward trend As for your motivation or ability to pay, you should be treated as a potential risk before you become one.

Predictive analytics models can determine payment patterns that indicate that a customer is having difficulties. For example, you can start with just a few days late or pay two instalments at a time. Any variation of the usual payment schedule should be a red flag for the system.

You can start a mechanism that fires when an unwanted pattern pops up. The system can communicate with the customer and ask if he needs help if he's going through a difficult financial time to offer solutions before the debt starts to pile up.

Forecast Cash Flow

Any business wants to know what to expect from future cash flows. Financial institutions are no different, and predictive analytics can help them make more accurate projections when it comes to expected accounts receivable.

Given that in the risk assessment part the model was able to identify customers that have the potential to delay or fail completely, it can be said that customers are expected to pay.

The business models of the debt collector depend on the ability to predict the success of collection operations and evaluate the results at the end of each month, even before the start of the billing cycle.

This helps to redirect the workforce so they do not focus on customers who would pay anyway for those who are unlikely to meet their obligations.

Increase the Relationship with the customer 

Predictive analytics can not only inform which customers represent the greatest risks to your business but also identify when it is best to contact them for the best results. For example, if they work in shifts, it is best to call them when they are not working or resting, which may be outside normal business hours.

Showing your customers that you are interested in their habits and lifestyle increases their chances of listening to their call agents and ultimately increase collection rates. Of course, to train the system, you need records of past conversations.

Predict Call Value

As with any business, debt collectors aim to maximize their ROI, allowing callers to focus on the most promising accounts, rather than simply following a list approach. Using predictive analytics, as described above, the model can assign a probability score to each potential call and classify them into the company's CRM.

The model generally works by calculating the probability of repayment by taking the event "call" as a modifier. This means, in fact, calculating the probability when the client is not called and the probability when a call is received. This is a very simplistic approach but highlights how such models can make a difference in the end result.

Possible Challenges

As with all big data models, the main problem is related to data cleansing. As it is a matter of getting into the trash, taking out the trash, before making a forecast, the company that handles this task first needs to build the channel to bring the date, clean it up and use it to train the neural network.

Another challenge may be related to various regulations for the protection of personal data and privacy issues.

Ready, set, Predizer

To put it all together, it's worth mentioning that predictive models can make a difference when it comes to revenue for debt collection agencies. You can increase conversion rates by targeting the right people at the right time. Of course, an in-depth analysis could go so far as to identify the message that could most influence the customer.

Targeting the right customers also means more productivity for call center agents and fewer lost work hours. Having a mathematical model behind the decisions eliminates prejudice and makes the process fair.

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