According to allied market research report “The global data analytics in banking market was valued at $4.93 billion in 2021, and is projected to reach $28.11 billion by 2031, growing at a CAGR of 19.4% from 2022 to 2031.” (Ref.)

The banking, financial services and insurance industry, and its consumers generate an enormous amount of data on a daily basis. This sector alone generates over 2.5 quintillion data bytes each day and it still has potential to grow. Data analytics plays a crucial role in this sector in innovation, improving customer experiences and improving financial operations.

Here are some of the key trends of 2024 in data analytics

1. AI-Powered Chatbots and Virtual Assistants
When was the last time you went into a bank or spoke to a human relationship manager for something? The banking sector has adopted Artificial Intelligence (AI) and machine learning and are revolutionizing customer service. Chatbots and virtual assistants have become bare necessities of banking and today, we can’t imagine banking transactions without them. These virtual agents use natural language processing (NLP) and predictive analytics to help customers in real-time. They provide personalized assistance, answer questions and doubts, and guide them through various banking processes. With the help of these AI-driven interfaces, BFSI organizations are improving customer engagement, reducing response times, and driving operational efficiencies. They also help banks in generating reports and providing recommendations to customers based on analysing their spending habits, budgeting and savings. With AI, designing individual financial products is an easy game.

2. Advanced Customer Segmentation
With digital adoption, managing a relationship with existing customers and attracting new customers are a challenging task. Therefore, BFSI companies are using advanced customer segmentation methods to design individual products and services to larger crowds. This is possible with the help of data analytics by gaining deeper insights into customer behaviour and preferences. By analysing customer data like transaction history, online activity, and demographics, banks can create targeted marketing campaigns, personalized products, and services for individual customers, gaining their satisfaction and loyalty. These technologies are used to build trust and build a long term customer relationship. Facial recognitions, voice recognition and fingerprint scanners have made the new customer on boarding process a cakewalk to customers. By developing user-friendly websites and apps, banks help customers understand complex banking transactions, investments and money management.

In the wealth management sector of this industry, data analytics helps provide hyper-personalization to customers. This allows for customized investment planning and guidance to meet the individual customer’s budget and goals. Wealth management companies use data analytics to analyse customers’ financial data, their economic status to design specific personalized investment options, improve asset allocations and provide real-time updates to them showcasing the transparency. All of this is possible without physically meeting the customer, through app, website and phone conversations.

3. Risk Management and Fraud Detection and Prevention
Banking, financial services and insurance sectors depend on data analytics and predictive modelling to detect abnormalities and to reduce fraud and financial crimes. They do this by analysing large volumes of transactional data and identifying patterns which show or indicate fraudulent activity. Organizations can detect suspicious transactions in real-time and take immediate action to prevent losses by blocking the transactions, sending out automated voice calls to alert customers etc. Data analytics plays a major role in risk management and compliance practices within the BFSI industry. With predictive analytics and machine learning algorithms, organizations can assess and measure various types of risks like credit risks, market risks and operational risks accurately. This helps in framing risk mitigation strategies, decision-making, and regulatory compliance.

Predictive analytics algorithms can forecast fraud trends and implement measures to mitigate risks, safeguarding both customer assets and organizational reputation. This majorly applies in loans and investments to avoid loan defaulting and wrong investments. For example, if a company has issues with the SEBI or doesn’t follow regulations, then when an individual tries to buy the stocks, he/she will be notified of the foul play.

Data analytics is shaping the future of this industry. It is revolutionizing the banking, financial services and insurance industry by facilitating organizations with data-driven insights to bring in innovation, improve customer experiences, and processes.

CRG Solutions is a Business Performance Improvement company, helping organizations traverse their Data to Insights journey and beyond. We are one of Tableau’s longest standing Gold Partners with vast experience in Visual Analytics best practices. Collaborate with us today to gain maximum benefits from your data.

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