Home Finance 5 Ways Banks can Use Machine Learning Today

5 Ways Banks can Use Machine Learning Today

Thanks to the massive volumes of data available in this current digital world, machine learning can change the experience delivered by financial institutions entirely.

Previously, it was a tool that was only available to international companies and tech giants on the Fortune 500 list. Currently, it is accessible to financial companies of all sizes, including credit unions and community banks.

Machine learning encompasses an area of computer science that utilizes large-scale data analytics to create dynamic and predictive models. It involves feeding the computers with the right data and algorithms.

Presently, machine learning technology is spearheading innovation in every sector, more precisely the banking industry. In fact, the banking industry boasts access to extensive archives of historical data and records, which is ideal for machine learning and artificial intelligence applications.

Machine learning can play a vital role in numerous stages, especially in the banking industry.

So far, it has helped in determining the lending risks of clients based on their credit history and income. Herein are some machine learning applications that can boost service experience in the banking industry

MORE: 10 Applications of Machine Learning in Finance

MORE – Data Science – 8 Powerful Applications

1. Enhanced Cross-selling and Marketing

Machine learning can be utilized as a powerful tool for creating more detailed insights into prospects and customers. Since it draws a wide range of external and internal data, marketers have more insights to work with than in the past. In addition, customers can be more accurately segmented based on their probable needs and profile. In turn, this advantage provides new opportunities for cross-selling and up-selling.

2. Automating Chatbot Conversations

With the rise of conversational interfaces and chatbots in the banking industry, machine learning can be used to improve customer service. Chatbots are virtual assistants that have robust natural language processing capabilities with specific client interactions. By allowing customers to access information quickly, they not only improve service levels but also offer a unique advantage over other entities that need customers to send an email or pick up the phone.

READ MORE – Top 25 AI Software for the Banking Industry

3. Forecasting Reasons for Customers’ Calls

Predicting call reasons serves as another application of machine learning in the banking industry. With the number of customer calls increasing on a daily basis, the number of calls being channeled to incorrect departments has also grown.

Machine learning can be used in predicting the reason behind a customer’s call based on the time it is made among other factors. Furthermore, the technology allows the forwarding of calls directly to the appropriate department.

4. Fraud Detection

Today, the data security risk is higher than ever due to the integration of the Internet, computing power and the growing volume of valuable data being stored online. Machine learning systems can identify anomalies in online behaviors and activities, as well as flag them to allow security teams to conduct further investigations.

5.Improving Collections

Currently, most customers go through a bad experience with credit companies due to being contacted unnecessarily by collections teams. With machine learning, this issue can be resolved, as it can predict those borrowers with arrears who can pay their debt without support or contact from the credit company. Doing so would help in improving the customer experience as well as saving time and money used by collections teams.

Source FinancialBrand

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KC Cheung
KC Cheung
KC Cheung has over 18 years experience in the technology industry including media, payments, and software and has a keen interest in artificial intelligence, machine learning, deep learning, neural networks and its applications in business. Over the years he has worked with some of the leading technology companies, building and growing dynamic teams in a fast moving international environment.
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