Top 5 Machine Learning Use Cases for Financial Industry

The financial industry has always been one of the most powerful influencers and consumers of technological innovation.
The banks were the first to leverage trains for postal services, introduce fire alarms and CCTV surveillance. Now they do the same with Machine Learning. Machine learning (ML), as a part of greater Artificial Intelligence (AI) industry, evolves rapidly. While it reached several significant milestones already (like Deep Blue defeating Kasparov 20 years ago, or Google’s DeepMind algorithm AlphaGO defeating the GO champion Lee Sedol in 2016, or Google’s algorithm trained to tell cats from dogs and used to identify skin cancer), the business needs more practical — and, yes, by “practical” we mean “feasible” — ways to apply ML in daily operations.

This time has come, and today we will tell you of top 5 Machine Learning use cases for the financial industry, so you know why venture capitalists and banks invested around $5 billion dollars in AI and ML in 2016, according to McKinsey.

Customer self-service portals

Call Centers are a thing of the past, as the generations of computer-savvy people enter the banking world. Both Millennials and Gen Z prefer customer self-service portals and mobile banking through their smartphones. According to Business Insider, more than 44% of US consumers prefer interacting with chatbots, not with human representatives. Deploying ML algorithms to serve as chatbots might allow saving up to $27 billion in salaries in insurance and financial sectors. This also excludes the human error factor, as ML algorithm never sleeps, never yells at the customer, constantly learns and becomes only better over time.

Risk Management in Banks and Financial Institutions

Risk management is another field where implementing ML can lead to drastic improvement of results. When the algorithm is able to analyze the whole variety of incoming data (both internal, from the company business flow, and external, like customers’ search queries and social interactions), both beneficial or potentially dangerous trends can be recognized and exploited or mitigated. Yet another brilliant research on the future of risk management in banks from McKinsey states that “risk management is crucial for success and financial health of the banks and other institutions; ML is the key element, allowing to evaluate all the factors to make a well-grounded decision, and every new piece of information processed by the algorithm makes its predictions more accurate.”

Compliance checks

Many regulatory documents like the USA PATRIOT Act require a thorough and in-depth analysis of the financial background for many customers in many types of transactions. This means every financial institution has to employ a compliance team that has to constantly analyze and update a huge pool of information regarding their customers and prospects. Add the splits, mergers, and acquisitions into play, and you will clearly see why this is a hell of a job. Implementing ML algorithms can help the compliance department direct all the influx of data from various disparate systems and sources to a centralized storage, to be stored, processed and exploited efficiently. This will bring the customer screening to a much higher level.

Credit Portfolio Management (CPM)

Monitoring the health of credit portfolio is equally, if not more important to checking the background of the prospects. Leveraging the power of machine learning for data analytics allows for a much more precise predictive and prescriptive analysis. This will help predict what customers are at high risk of canceling their deposits or defaulting on their loans. As a result, the bank executives will be able to intervene and prescribe a much more personalized approach, keeping the credit portfolio healthy and drastically reducing the percentage of irretrievable loans. Yet another McKinsey report describes the growing role of AI in retrieving valuable insights from the unstructured data and the benefits it brings for banks.

Robo advisors for wealth management

Being wealthy doesn’t always mean being well financially educated. Rich people often prefer delegating their wealth management to trusted financial institutions. When all the data about such fortunes, along with all the transactions and events in various important fields is processed by a robo advisor, the consulting financial structure is able to make swift actions. Such actions might include selling dangerous assets or buying cheap once the opportunity arises, thus ensuring optimal allocation of the customer’s resources and all-around good performance of the assets. Blackrock, one of the world’s-leading investment management funds, has published a detailed report regarding the efficiency of employing robo advisors for asset management.

Concluding thoughts

Financial institutions have to follow the latest technology to secure their bottom line and provide the best service to their customers. About a decade ago, offering an online service was the way to gain a competitive edge. Around 5 years ago a mobile app became an essential component of a good offering. Nowadays, employing an AI algorithm is what can push the business ahead of less tech-savvy competitors.

Hopefully, our article on top 5 machine learning use cases for financial industry provided some important insights for you. Did we miss something important? Do you have a hands-on experience with applying ML in banking or financial services? Please share it with us!