Artificial Intelligence in Insurance and Finance

Artificial Intelligence (AI), that concept that everyone talks about but very few can explain, is changing the way business is outlined, especially in the banking and insurance industries.

We could say that AI is nothing more than the ability of our computer systems to perform their own learning from experience. This concept breaks with the current model in which our computers execute exclusively the instructions that have been provided. Thanks to AI it is now possible to process massive amounts of information. In order to recognize certain patterns in which the systems will base their own decisions, following a process of continuous learning. If we consider that we live in a world in which huge amounts of information are generated daily, we will realize the importance of AI, only if these organizations want to benefit from the data.

Modern AI techniques, applied to internal data managed by organizations, allow banks and insurers to improve in two directions. On the one hand, they help them reach their customers better, offering them a higher level of personalization, and increasing loyalty (consumption prediction, up-selling, cross-selling, prediction of customer churn, product customization, etc.). On the other hand, they help them improve their processes, to be more efficient and, consequently, to improve their results. In this sense, AI ​​can become a great ally against one of the phenomena that most directly attacks the P&L statement of banks and insurers: fraud.

Fraud, in all its forms, is a phenomenon with an increasing impact on organizations. It is enough to say that the rate of fraud in claims (in the insurance sector) have doubled in just seven years. And it continues to grow. In the case of the banking sector, the new scenario in which almost any service can be carried out telematically, in many cases providing digital documentation, opens a whole world of opportunity for people to commit fraud.

AI has emerged as an essential tool in this permanent fight against fraud. In fact, AI in general, machine learning and even automatic learning in particular, can help organizations in multiple ways to control fraud in all or many of its variants. Let’s see two concrete examples.

Management processes for claims in an insurance company, (or for requests for financial services in a banking entity), are based on simple and static rules applied to internal information of the company. But what would happen if organizations were able to enrich this information with external data from the Internet and replace those static rules with an intelligent system that is capable of learning after each operation? The Internet, and social networks in particular, hold very valuable information that can be used to detect evidence of fraud both in claims for accidents and in the contracting of financial services. The problem so far was the impossibility of processing such enormous amounts of information. However, currently, AI techniques allow processing large volumes of data, extracting those that are really useful and creating dynamic patterns that allow a precise detection of evidence of fraud. Therefore, avoiding compensation for a rigged loss or avoiding the granting of a high-risk loan are real and tangible benefits provided by the use of AI.

Another interesting example of the application of AI techniques in the fight against fraud is related to the search for evidence of manipulation in digital documents. Currently, many of the business processes, both in the insurance and banking industries, involve digital documentation from customers or suppliers. Identity documents, refundable invoices corresponding to medical services or photographs of losses in the case of insurers, and identity documents, pay slips or labour reports in the case of banks, are clear examples of documents that can be manipulated. Through AI techniques it is possible to process these documents, accessing their metadata and internal marks, anonymizing them, modeling them and extracting patterns that allow the system to learn to detect possible inconsistencies and/or manipulations. As in the previous case, AI offers a direct return to the organizations, either by avoiding the payment of medical services that have not taken place, avoiding the payment of claims of non-existent losses or by limiting the granting of loans with risk of default.

In conclusion, AI has become a mandatory tool for insurance and banking companies in their fight against fraud. It provides organizations with very important quantitative, since it allows to process huge amounts of information in record time, as well as qualitative advantages, since it is able to learn from the data it processes, achieving a continuous improvement of the results.

Perhaps someone may think that the benefits of AI are more theoretical than practical. My own experience at Treelogic, a company specialized in this area, has shown that nothing could be further from the truth. Treelogic’s anti-fraud platform processes more than half a million of auto claims per year applying the most innovative AI techniques. The results speak for themselves: between two and five percent of the analyzed claims turn out to be fraudulent and, what is more significant, the system begins to generate patterns that allow the identification of fraud networks. Is there any doubt about the return of AI?