The Machine learning wave in the financial services


In this article, I am going to talk about how machine learning is transforming the financial services industry. In a typical economy, the financial services sector is made up of banks, insurance companies, real estate firms, credit and payment processing companies who serve both the retail and commercial consumers. All these firms have enormous volumes of historical data of their customers due to a large number of transactions they process daily. I will begin by defining what machine learning is and then relate it to the large datasets in the financial services firms.

Machine learning is a subset of data science that uses statistical models that learn from experience (data) without being explicitly programmed. The modes then draw insights from the data and make predictions when subjected to a different dataset. Simply Machine learning involves training a model using a dataset and then evaluating the model by fitting a test dataset to check its performance. Think of teaching a small child simple arithmetic computation such as addition, subtraction using a set of examples then subjecting the child to an evaluation test after the learning process.

Having understood what machine learning is, let’s look at some of the major applications of machine learning in the financial services sector.

Underwriting and Credit scoring

Underwriting is the process that banks use to decide who to give credit to, how much to give and when to give. In insurance, it’s the process of gauging the risk associated with insuring a certain customer. Traditional underwriting models relied on limited data points about the customer mainly numerical features. This greatly limited their accuracy levels resulting in underwriting losses.  Over time banks and insurance companies have been able to collect large volumes of historical data about their clients from different sources e.g. social media, CRMs, etc. This data has been used to train machine learning models that utilize thousands of data points about the customer to predict the credit score at high precision. This has greatly reduced underwriting losses previously incurred by the banks and insurance firms. 


Robo-advisors are digital platforms that provide automated, algorithm-driven financial planning services with little human supervision. Machine learning algorithms are being used in Robo-advisors for wealth management and product recommendation. They typically perform the duties that a human investment advisor does. Robo-advisors have helped mitigate the subjectivity associated with human advisors, have lower charges compared to management fees paid to investment advisors and recommend personalized products that improve customer experience.


The financial services sector has experienced a lot of transformations over time. From the old manual bookkeeping to excel spreadsheets, to modern-day sophisticated accounting software. There still exists manual repetitive tasks within the industry despite the developments in departments such as call centers.  Applications such as chatbots which are based on natural language processing that relies on machine learning algorithms have been developed to automate the repetitive tasks in customer support centers. Well trained chatbots can handle 80% of the call center tasks. This has greatly saved on human labor costs and improved customer experience since the bots can be accessed when the customer support representative is not available in the office.

Fraud detection

Financial institutions have been prone to fraudulent transactions which result in financial losses. Traditional fraud detection and prevention techniques employed by these institutions such as whistleblowing and internal checks have proved futile over time. Deployment of machine learning algorithms in monitoring the numerous transactions has helped identify fraudulent transactions in real-time and flag them off. With the large data sets available to these firms the models are well trained and thus have high accuracy scores. The models have helped save revenue lost due to fraudulent transactions.

Case studies

  1. Citi bank: the bank’s heavy investment in FeedzAI. Feedzai is a fraud detection machine learning platform that continuously scans large volumes of data to recognize emerging threats and alert customers in real-time. It also offers customers on the digital platform protection against cyberattacks through its user-friendly omnichannel support.
  1. Zendrive. It is a mobile app that monitors the driving behavior of customers to potentially offer them significant discounts on car insurance premium. This is in contrast with the current models of insurance pricing that use variables like age, education, marital status, etc.

Industry insights

Captricity has developed Machine learning algorithms that are able to extract handwritten or typed forms into a digital form with 99.9% accuracy. These algorithms are helping insurance firms reduce cycle times.

Conclusion and recommendations

The value of Machine learning is growing daily and it is hard to imagine the financial services sector without utilizing it in the near future. It’s evident the numerous advantages of incorporating the algorithms in the daily operations of the sector. Though implementing the whole process can be costly and time-consuming a simple implementation of the initial stages such as solid data engineering, aggregation and visualization then applying the open-sourced machine learning frameworks can effectively perform similar functions to the purchased software and drive the business forward. 

Thank you for reading through the article. I hope you have learned something.


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