AI in the Banking and Insurance Sector

Banking/Insurance


Nowadays, companies in the banking and insurance sector are faced with the need to process large amounts of data in order to react quickly to changes in the market. In particular, the economic crisis linked to COVID-19 has transformed the way companies in the banking and insurance sector act. Today, capital requirements have changed and credit supervision is more regulated. In this context, companies need to make operational use of all their data in order to optimise decision-making.



Challenges encountered

Today, one of the biggest challenges faced by banks and insurance companies is the growth in the volume of data which requires the application of data science solutions to better detect various anomalies, such as:

  • One-off anomalies related to the management of a very large amount of data in the context of transactions sometimes involving billions of dollars

  • Contextual anomalies concerning abnormal cases in a certain context, such as a transaction exceeding the authorised credit limit.

  • Collective anomalies concerning several data sets or parts of the same data set that appear abnormal, such as simultaneous transactions in different parts of the world.


The usual practice of detecting anomalies that its companies may encounter is to put an end to them as soon as they occur. This may require serious fuzzy matching and merging adjustments to ensure accuracy:

  • Fraud detection (credit cards, insurance, etc.) which must be done in real time so that it can be stopped as soon as it occurs. Attention must also be paid to false positives that can disrupt the user experience.

  • The analysis of the stock markets, which have a wide variety of data at their disposal. This may require serious fuzzy matching and merging adjustments to ensure accuracy.

  • The detection of insider trading, which requires real-time intervention.


Risk management covered by papAI

Our aim is to improve risk management (external fraud, internal fraud, managerial fraud) of companies in the banking and insurance sector in order to stop fraudulent behaviour, limit losses and optimise their ROI. Our algorithms enable the identification of these anomalies, whether they are one-off, contextual or collective.


COMMERCIAL BANKING

  • Fraud detection and prevention

  • Importance of interpretability, an ethical and transparent vision as opposed to the usual AI "black boxes".

  • Combating money laundering and wire transfer fraud: Detecting fraud, reducing the number of people doing manual work and reducing false positives

  • Automation of regulatory reporting

  • Cyber security


INVESTMENT BANKING

  • Cash management risk : - Predictive analysis to identify risky invoices, double payments, etc. Optimization of internal communication (finance, operations, middle and back offices) with data analysts to accelerate problem identification.

  • Prediction of business failures: Predictive analytics enables potential failures to be automatically flagged and remedied, reducing the impact on customer satisfaction and regulatory implications.


Use Case


Financial scoring of companies

Evaluation of companies on a periodic and self-study basis while assigning a score in order to offer business support services


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