How Machine Learning is Used in Banks: 9 Successful Cases Around the World

The Learning Machine ( machine learning ) and the banks: how financial corporations all over the world make a lot of money on the analysis of large data using the methods of artificial intelligence.

Machine Learning In Foreign Credit Institutions

1. The American bank JPMorganChase has developed an automated machine learning model for analyzing documents and aggregating important data from them, which made it possible to process 12 thousand loan agreements in a few seconds, although earlier it took about 360 thousand man-hours.
2. The opening of fully automated loan offices in another American bank, Bank of America, has reduced the cost of maintaining new offices by several times. Their area is 4 times less than usual, and instead of employees – smart ATMs and screens for remote video communication with employees of other branches.
3. Another well-known bank in the United States, Goldman Sachs, has cut the costs of wages for traders by 300 times, leaving only 2 employees instead of 600 and automating the processes of buying and selling shares. Also, this financial corporation has successfully implemented software to automatically combine credit card balances .
4. The artificial intelligence system used by the Chinese debt collection company Ziyitong shows a 2-fold increase in the recovery rate for loans overdue by up to 1 week: 41% instead of 20%. This result was achieved through the collection and analysis of data about borrowers and their friends and the subsequent conversation of the dialogue robot with the debtor on the phone. The conversation is automatically recorded and analyzed, and the wording that is most likely to affect the borrower and make the debt repayable is determined. In addition, the system contacts the debtor’s friends and asks to return his money through friends.

Machine Learning In Banks

Here  we have already written in detail how the methods of artificial intelligence, machine learning and big data analysis helped Sberbank to earn about $ 3 billion in a year. However, this is not the only example of the successful application of modern information technologies in the domestic financial sector. Other lending institutions are also showing excellent results in this area, in particular:

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1. the work of the contact center at the Otkritie Bank has been accelerated by 2 times. 
2. the maximum time required for making a decision on a loan at B&N Bank has been reduced by 3 times.
3. expenses for collection at the Khlynov bank were reduced by 1.5 times. 
4. the implementation of the anti-money laundering and terrorist financing system at Tinkoff Bank increased the detection of suspicious transactions by 95% and reduced the number of false positives by 50%. This was achieved through machine learning algorithms, automating the creation and preparation of reports, sending notifications, cutting off obviously correct accounts and transactions from the sample under study. As a result, the bank redistributed resources from mandatory control to direct investigation of criminal schemes.

These examples show the global trend towards total banking robotic process automation and widespread implementation of Big Data and Machine Learning tools in all areas of the financial sector.So that in a couple of years the robot does not take your workplace, move along with new technologies – become a professional in the field of big data analysis and machine learning! To do this, come to our hands-on courses for engineers, financiers and leaders, where, using specific examples, we learn to select, configure and administer methods and tools for working with Big Data and Machine Learning.