Any seasoned professional in the BFSI industry will tell you that providing customers with an exceptional experience is paramount. In this case it really comes down to dollars and “sense.” Customers want ease, options for banking, and overall value when it comes to financial products and services. What’s the best way to gauge the customer’s experience and make sure their money (and loyalty) remains with your institution?
The simple answer is data. The complexity is understanding the data and learning how to use it to enhance the customer experience. Companies are leaning on artificial intelligence (AI) and machine learning (ML) to collect and analyze large volumes of data to support customer growth and increase operational efficiency.
According to a study by Global Market Insights, AI in the BFSI market is forecast to surpass $140 billion by 2028. The real advantages to using AI/Ml are to measure and enhance the customer experience and to potentially keep more money within your institution. Let’s find out how.
AI and ML Use Cases in BFSI
1. Chatbots for Customer Service
One of the most widely used AI applications is chatbots. For businesses, chatbots deliver a high ROI in cost savings. On the customer side, chatbots provide an individualized user experience where customers can get answers to their financial questions. By providing a personalized experience, these bots can be deployed on apps, websites, and other customer interaction checkpoints.
Customers use intelligent banking AI chatbots for queries relating to balance inquiries, statements, expenses, insurance plans, loan eligibility, etc.
One example of a successful chatbot is Erica, Bank of America’s virtual financial assistant which launched in 2018. Since that time, Erica’s interactions with BOA clients have exceeded the 1 billion mark.
Chatbots provide essential customer service 24/7. The simulated conversations linking institutions and customers further enhance the overall customer experience. With the ability to gain precise answers to their questions, customers develop a sense of trust in their financial institutions and are apt to practice long-term loyalty.
2. Next Best Action Recommendation
A unified cross-product can allow the banks to create new cross-channel personalization solutions that will become a source of income and engagement in the dynamic marketplace. Furthermore, there can be more data backed accuracy on decisions such as whom to target, what to target, how (channel) to target, when to target based on Machine learning.
3. Introducing Seamless Automation
Traditional manual intensive banking activities e.g., Underwriting process can be substantially decreased with AI automation. This can result in reduced workload for banks and their employees, 24×7 service, and a high degree of customized services.
4. Assessing Risk Management in Real-Time
Banks are in the business of lending money. With that comes the responsibility of lending to individuals with the hope they won’t default. How do banks weigh the risk? Institutions rely on data to accurately estimate the credit worthiness of any potential client. Advanced analytics can be used to discover characteristics like credit history, income, and market conditions. Then, AI/ML algorithms can assign risk scores for customers and ultimately predict which clients are likely to default on their loans.
Data Science helps banks enhance risk assessment where relevant insights can be extracted in greater detail for assessing the risk profiles of the credit applicants and devise better collections planning through delinquency-based strategy focusing on high-risk customers. Data science forms a critical part of the operations that help in reducing risk by identifying, prioritizing, and monitoring them and reduce potential monetary losses.
5. AI-Based Fraud Detection
Recent statistics show the importance of AI in the financial services industry with fraud detection ranking as the most important use case of AI among respondents. It’s no wonder with 2,527 cyber attacks worldwide in the financial industry in 2021. Continuing to dominate concerns are credit card fraud, financial breaches, and money laundering. Thwarting cybercriminals is key to avoiding monetary disasters and keeping customer financials private and secure.
AI aids in the prediction and prevention of cyberattacks by improving system effectiveness, helping to detect suspicious activity and adding an additional layer of security. Using historical data, institutions can learn from past threats to detect, mitigate, and prevent both real-time and future attacks with greater accuracy. Apart from external threats, AI can also effectively monitor internal operations, preventing data theft and abuse.
The detection and suppression of fraudulent activities related to bank accounts, cards, transactions, and other irregularities are tackled by machine learning algorithms. For instance, if a customer does an unfamiliar transaction from a new device, the system may ask for additional security questions to ascertain identity.
6. Digital Marketing Effectiveness
In 2021, 4 out of the top 5 digital marketing objectives related to new customer acquisition. By analyzing data, institutions gain a greater understanding of customer value and needs. ML algorithms can analyze web activity, ad campaign responses, and mobile app usage. With the help of analytics and insights into customer transactions and trading activities, banks can understand their clients in-depth while delivering the best services.
7. Growing the Customer Relationship
Proactive customer experience (CX) monitoring can result in higher levels of engagement and retention. With this information, BFSI organizations can create effective marketing strategies that support customer retention and appeal to new audiences. ML algorithms can also be used to analyze user behavior and develop custom, individualized offers. Personalized targeting effectively grows customer relationships and opens additional revenue streams.
8. Market Trend Prediction
As an untapped asset, data has no inherent value. AI uncovers opportunities that legacy technology does not have to ability to detect. By using AI and ML, institutions can detect data patterns which allow them to gain a better understanding of metrics relating to operational data and helps them to capitalize on customer opportunities in relation to sales.
Take, for example, the Coronavirus pandemic. We know now that it triggered a 200% increase in new mobile banking registrations and an 85% rise in mobile traffic. Most major banks were able to predict this major market shift with the help of AI prediction. In response, they ramped up their digital banking offerings and set aside enough capital in case loans went bad. This helped them satisfy their customers while propelling their digital transformation efforts. For banks that weren’t leveraging AI for market trend prediction, they couldn’t weather the storm.
Making the Case for AI/ML in BFSI
Customers are the center of the BFSI universe. Launching an AI/ML strategy can help financial institutions differentiate their offerings and elevate the way they engage with their clients. Chatbots, predictive analytics, risk management, cybersecurity, and digital marketing are just a few examples of how AI is being leveraged in the BFSI industry to improve both customer relationships and internal operations. As leading businesses in financial services look to continue their digital transformation to respond more quickly to changing market dynamics, fend off new fintech competitors, and build stronger customer loyalty, AI/ML has emerged as the key enabler.
Apexon helps enterprises to take advantage of the data they collect by using advanced analytics, AI, and ML to create practical applications that provide the insights needed to enhance customer experiences, accelerate product lifecycles, improve resource allocation, and increase operating efficiencies. To learn more about predictive analytics, check out Apexon’s Advanced Analytics and AI/ML services or get in touch with us directly using the form below.