Seven Common Use Cases for Machine Learning and Artificial Intelligence in Fintech

The proliferation of data and mobile technology has afforded the banking and financial services sector the opportunity to benefit from Artificial Intelligence(AI) and Machine Learning(ML). Modern customer expectations for real-time and seamless user experiences necessitate the use of automation, predictive analysis, and optimization of product and service experiences. In today’s technology-driven world, financial institutions are well positioned to improve customer engagement, reduce resource expenditure, and improve efficiency through data-driven solutions.
We’ve identified seven use cases where AI and ML solutions can help to automate and augment fintech and banking products and services.

1.) Using AI to automate internal processes can create efficiencies that reduce time and costs in comparison to manual processes. Data scientists are able to automate the manual processing of forms using optical character recognition (OCR). This automation reduces the resources needed for data entry and processing. Similarly, using natural language processing (NLP) of the text of complaint calls, emails, or other types of communications, organizations are able to expedite compiling and processing complaints. Using this method, customer complaints become easily accessible and are categorized by topic or seriousness for reporting.

2.) Automated call routing and chatbots have transformed customer service. AI-powered chatbots can handle greater numbers of customer service needs, reducing the number of high-cost touch points. They give the majority of users a high-quality personalized experience, allowing customer service experts to handle a few exceptional needs. Similarly, call routing based on customer characteristics and behaviors allows for the customer service experience to be focused, personalized, and more efficient.

3.) Predictive analytics can enhance the customer experience and personalize marketing. Using known characteristics of a customer, their previous behavior, and their interactions with technology; machine learning algorithms can create predictions about future choices and make customer experiences more streamlined. Using product interaction data and analytics, data scientists are able to find the optimal user experience for increased conversion and client satisfaction. Predictions of future decisions and behaviors can also create ways to personalize products and services that are most likely to be the best fit for a customer or future customer. Organizations can predict which customers are most likely to experience a life event, like a new job, that would necessitate a specific financial product offering.

4.) Churn prediction is an AI capability that allows financial institutions to predict costly employee turnover as well as customer churn. Using machine learning algorithms, organizations are able to predict employees or candidates who are most or least likely to leave the company in the near future. This also works to predict the customers most or least likely to discontinue using services or products and optimal incentives to make them more likely to continue on as a customer.

5.) For companies who want to offer their customers an in-person presence in addition to a digital presence, machine learning can predict the optimal location(s) for ATMs or bank branches in order to service customers more efficiently and maximize the use of those locations.

6.) AI-powered tools can help financial organizations augment an existing risk mitigation system with machine learning analysis. Whether the customer is an individual or a company, algorithms are able to identify characteristics and behaviors associated with clients who are more or less likely to repay loans. Financial institutions are able to reduce their risk by analyzing the data of potential borrowers to predict their future financial behaviors.

7.) By leveraging AI tools, data scientists are able to create prediction algorithms to set a foundation for detecting fraud or augment an existing system to continually improve fraud detection. These algorithms can detect anomalies, potentially fraudulent activity, in a client’s account based on the regular pattern of behavior. On a large scale, activities that fall out of the typical for an individual account would be flagged as suspicious and alerts would be created.

The banking and financial services industry has a critical challenge and opportunity to be more efficient with resources, processes, and customer service engagement. Current technology-driven customers and their high service expectations necessitate timely adoption of data-driven solutions and decision-making, including AI and ML.

Contact our multi-disciplinary team of scientists and strategists at Valkyrie to learn about AI and ML applications in your industry’s space.

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