Wouldn’t it be nice to predict the behavior of your customers?
100% Yes!Â
Today, during another webinar on the factoring topic, that we’ve been organizing with efcom for years, and personally Federico Avellan Bergmeyer – Chief Partner Officer at efcom, we discussed the topic of risk management in the receivable finance industry, and particularly – how AI-based predictive analytics can help in mitigation of risks of fraud, late payments and non-payments.
If we look at the Risk Management models that are used right now in the main part of financial organizations – so-called Conventional Risk Management.Â
It’s based on the following:
- Financial Statement. Performance analysis is based on past financial statements.
- Credit Agency scores, which are also based on historical data.
- Payment behavior (again, based on historical payment data).
- Industry analysis, describing how historically the industry your customer operates in behaved.
If we think of the availability of real-time data we can rely on in our decisions, we ‘d be able to use AI and ML-based tools in Risk Management, which means the following:
- Process Big Data. Of course, for that data should be collected first.
- Detect complex patterns to understand the correlation between available data.
- Adopt market changes.
- Automate data tasks.
- Reduce bias.
The predictive analytics process looks the following way:
In this example, Federico presented how they at efcom created the solution called CubX which uses machine learning algorithms to analyze data, including credit scores, financial statements, and customer payment history. While most risk-preventing instruments offer such or similar functionalities with historical data sometimes older than one year, CubX does risk-preventing with data from today, from now, and in real-time. This allows us to accurately predict the likelihood of late payments or even default.
Here you can read more about CubX by efcom and vote for that solution
Watch the webinar recording to learn more:
