Our customer is a large commercial bank with a network of branches across the country, offering deposits, loans, and other services.
Detailed information about the client cannot be disclosed under the provisions of the NDA.
The primary focus of every bank or financial institution is the satisfaction and security of its account holders. As part of their daily operations, these institutions handle client accounts, oversee investments, maintain adequate liquidity, and perform other functions.
Unfortunately, the banking industry is currently facing a significant threat from suspicious and malicious activities that not only endanger customers but also the industry as a whole. Until recently, banks have used mostly manual rules-based systems, but with fraudsters turning more sophisticated, these systems are rapidly becoming inefficient.
One of the key American banks approached Innowise Group, seeking an effective machine learning in banking solution to detect and combat financial fraud. As the client expanded and the number of transactions increased, the bank periodically faced malicious activities that endangered its safety and reputation. Surely, our customer had an anti-money laundering system that prevented criminal profits from being camouflaged and incorporated into the financial system. However, it lacked accuracy, showing a high number of false positives and leaving room for account takeovers and payments fraud.
Initially, our project team developed business and technical requirements to meet customer expectations. Throughout the project, our business analyst maintained close contact with the client’s banking consultants to gain a deeper understanding of the client’s business and take full advantage of machine learning in financial services.
As for the ML solution, the most challenging aspect was achieving optimal metrics for users with different transaction histories. Our model was effective for account holders with substantial transactional history but ineffective for new users with a lack of historical data. Such users were treated as inactive accounts containing only identity information and no transaction history. Though this assumption eliminates the benefit of having complete user data, it, nonetheless, yields reasonably stable training results for the ML model.
After discussing the issue, we investigated “few-shot learning” methods that could enhance our metrics. We conducted a proof of concept but it did not result in the substantial improvements we anticipated. Therefore, our project team continued to enhance the platform and delve deeper into our client’s business domain. This enabled us to design features that significantly impacted the “few-shot learning” model, ensuring accurate prediction results for the banking machine learning project.
Our team followed the Scrum methodology, with three-week sprints throughout the project. We held regular meet-ups with the team via Microsoft Teams to monitor the progress of the project and accommodate any changes to the scope. As of now, the project has been successfully completed.
Innowise Group has developed an advanced ML-powered extension to detect suspicious or fraudulent activities and take proactive measures based on that. We ensured impeccable security and eliminated the risk of breaches and financial crimes. Overall, machine learning in banking and finance implementation brought the following benefits:
Machine learning algorithms rapidly analyze vast amounts of data. With the pace and volume of banking transactions growing, our machine-learning platform scrutinizes new information continuously.
Machine learning algorithms can execute repetitive operations and instantly spot subtle alterations in patterns. Our ML solution examines hundreds of thousands of payments per second, streamlining the entire transaction process.
In this project, we used machine learning algorithms that can be trained to identify patterns within seemingly trivial data. They recognize subtle or non-intuitive patterns that would be challenging, if not impossible, for humans to discern. This enhances the precision of fraud detection, resulting in fewer false positives and a reduced risk of unspotted fraud.
accuracy in reducing fraud
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After examining requirements, our analysts and developers devise a project proposal with the scope of works, team size, time, and cost estimates.
We arrange a meeting with you to discuss the offer and come to an agreement.
We sign a contract and start working on your project as quickly as possible.