Deploying Machine Learning on Logs in Banks

Log analysis involves large volume of data sets. Whether it is a Fintech start-up or a Fortune 500 company, they generate terabytes of log data every day. How can you make sense of all that data? If you are trying to use it manually, Good Luck to you! The only way out is to deploy machine learning to interpret log data in an automated manner. The field of log analytics has seen major advancements in recent years, thanks to Machine Learning (ML).

Find out how major banks and FIs are already using machine learning on logs to have better management of compliance risk, credit and fraud thus helping banks in automating processes, reducing risks, and improving customer engagement.

A Quick Snapshot on Machine Learning on Logs

Let me run you through a quick snapshot about machine learning. The term “Machine Learning” was invented by Arthur Samuel, a forerunner in artificial intelligence (AI) and computer gaming in the year 1959 at IBM. ML is a sub-set of AI that has an autonomous use of computer algorithms to help organizations crunch large volume of data sets that are faster than humans with higher accuracy standards.

Banks and FIs use ML to process a large amount of unstructured data by developing algorithmic patterns (supervised & unsupervised) that detect problems automatically without any human interference. They use log analytics tools in-house that require a robust infrastructure to train the ML tool for analyzing various logs to process a large amount of accurate data.

Application of Machine Learning on Logs in Banks & Financial Institutions

Banks and FIs are applying ML on logs to have better:

Compliance Risk Management

Banks and FIs are prone to data breaches. They face the heat of dramatically increased costs in implementing effective compliance management solutions. The key area of focus is to enhance their risk management frameworks that will help in combating current and emerging risks.

Implementation of effective compliance management solutions is the key focus areas for all financial institutions. As of today, AMLD IV, Basel III, BCBS 239, CRR/CRD IV, CRS, EU GDPR, FATCA, FRTB, IRRBB, MiFID II, PRIIPs, and PSD II are few of the regulatory mandates that every financial institution must comply with, that will help in streamlining regulatory projects.

Banks and FIs can deploy security log analytics techniques that enable security teams to take quick actions on malicious activity, minimize exposure to fines & lawsuits, and detect anomalous behavior. By bringing ML to log analysis, banks can monitor & alert risks; adhere to audit & regulatory compliance and security policy compliance; and track the response time to a security incident. This will make banks and FIs proactive in identifying potential threats and their root cause to mitigate the same. The ML algorithms applied to logs can help banks detect anomalies and recognize uncommon patterns before customers experience it.

Credit Risk Management

Many of the banks and FIs still follow the legacy model with regards to credit risk workflow, where human error and poor judgments are common. When customers apply for a loan/credit card, banks use ML credit default prediction models in alternative data sources such as text message activity, mobile phone usage, and utility payments. This will improve the loan rating accuracy and instant credit decisions, giving customers easier access to credit. Algorithms in the credit default prediction model help banks and FIs improve early warning systems (EWS), lower the rate of default losses, and reduce risks of losing customers to competitors due to a sluggish progression.

Fraud Management

Banks and FIs are using ML techniques to identify fraudulent transactions in different customer accounts. ML techniques have neutral networks algorithmic patterns that can recognize transaction size & frequency and the type of third-party vendor involved in transaction processes. This helps banks and FIs reduce false positives, which can enable prediction of fraudulent transactions, lowering costs and increasing customer satisfaction.

Banks and FIs use behavioral analytics as a fraud detection tool by enabling risk managers to detect fraud by continuously monitoring the activity across multiple channels. The main aim is to provide faster responses to customers. It can also understand the behavior pattern of the user while using an online or mobile account. The pattern allows banks and FIs to understand distributed attack made due to fraud.

Banks make use of log monitoring alerts with ML techniques that allow them to profile the device and users associated with the fraud. This can help banks to stop breaches and address fraud and other anti-money laundering (AML) activities. Banks should deploy application log monitoring system for better management of security vulnerabilities and address production application problems.

Conclusion

Banks can build up better security capabilities using ML techniques in the coming years to avoid risks and fraud. Is your bank/FI ready to uptake ML on logs to develop state-of-the-art capabilities for an effective security management system?