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, the banking sector is well positioned to improve customer engagement, reduce resource expenditure, and improve efficiency through data-driven solutions.
We’ve identified seven scenarios where AI and ML solutions can automate and augment fintech and banking products and services.
1) Automating internal processes using AI can create efficiencies that reduce time and costs in comparison to manual processes. Using optical character recognition (OCR), data scientists can automate manual processing of forms. This automation reduces the resources needed for data entry and processing. Similarly, data scientists can expedite the compiling and processing of complaints through natural language processing (NLP) of the text of complaint calls, emails, or other types of communications. Complaints are then more easily accessible and categorized by topic or seriousness for reporting.
2) Automated call routing and chatbots have transformed customer service. Data scientists can create chatbots which can handle greater numbers of customer service needs, reducing the number of high-cost touch points. Chatbots can give customers a quality personalized experience for the majority of users, allowing customer service experts to handle a few exceptional needs. Similarly, call routing based on customer characteristic 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 can experiment to find the optimal user experience for increased conversion and client satisfaction. Predictions of future decision and behaviors can also allow targeted, personalized marketing of products and services which are most likely to be the best fit for a customer or future customer. For example, data scientists could predict which customer are most likely to experience a life event, like a new job, that would necessitate a specific financial product offering.
4) Churn prediction can allow financial services companies to predict costly employee turnover as well as customer churn. Machine learning algorithms could predict employees or candidates who are most or least likely to leave the company in the near-future. Similarly, data scientists could 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 locations for ATMs or bank branches in order to service customers most efficiently or maximize use of those locations.
6) Data scientists can help you augment an existing risk mitigation system with machine learning analysis. Whether your clients are individuals or companies, machine learning algorithms can identify characteristics and behaviors associated with clients who are more or less likely to repay loans. Financial institutions can reduce their risk by analyzing the data of potential borrowers to predict their future financial behaviors.
7) Data scientists can create prediction algorithms to set a foundation for detecting fraud or augment an existing system to continually improve your fraud detection. Machine learning 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.