AI-ML and big data are the growth drivers of business and customer experience within BFSI sector

The BFSI industry has witnessed a paradigm shift owing to the technological evolution. Artificial Intelligence (AI) and Machine Learning (ML) are ways of the future in finance, as they combine the power of advanced data processing with the ability to tackle fraud and improve enforcement. Almost every industry including health, retail, travel and BFSI is exploring how artificial intelligence and machine learning can be used to help them. Frontier technologies like AI, ML, robotics, and biotechnology have shown enormous potential for long-term growth. COVID 19 accelerated the adoption of these technologies as companies learnt that it is technologies like these that look promising in the future. In 2020, businesses expanded their implementation of AI and machine learning, focusing on projects that result in increased sales and lower costs thus resulting into profitability. AI also allows financial institutions to process massive amounts of data at breakneck speed to extract useful insights. 

Artificial Intelligence: Fuel to The New Normal
Several industry leaders share this view that AI & ML is transforming customer engagement and transforming various businesses. Today, organizations have started considering AI to be the new normal as it is helping them improve customer interactions to achieve greater sales growth and automating processes to minimize costs. Most enterprises in the BFSI industry are channelizing efforts to build a digital and personalized model when it comes to engaging with customers with help of Artificial Intelligence.  AI-backed virtual assistants in the BFSI sector have turned the tide by enabling the delivery of personalized, relevant and real-time customer service. With the application of AI, customers are able to get answers to their concerns within seconds rather than the long hold through the helpline number. One of the major applications of AI in the BFSI sector is enhancing personalized customer engagement. Onboarding procedures have been streamlined as a result of the pandemic, greatly reducing client trips to the bank, and thereby enhancing the optimum use of human capital and costs.

Machine Learning: A key accelerator for growth
There have been several advancements in the field of ML over the last 12 months. Not only have we seen improvements in tooling, security, and governance requirements for organizations, but we’ve also seen significant changes in the market as a result of COVID-19’s economic impacts. Machine Learning is gaining significance in the BFSI industry due to its growing benefits. Machine-learning software can become smarter and can be adapted and updated anytime to keep up with the pace of business today. It can quickly perform routine operations, giving administrators more flexibility to focus on more complex problems rather than paperwork. Greater profits are realized as a result of automation around the board hence one of the major implications of ML is improved productivity and increased automation. A key task for the lenders is to assess the risks accurately. A precise digital footprint of each customer helps such institutions assist in reducing complexity for managers while dealing with specific customers. In fields like loan underwriting research, the computer method is more reliable than an individual, removing any potential human bias.

Big Data: Growing Relevance in the BFSI sector
Big Data technology helps a lot in increasing the efficiency, improving the pricing and competing with big business with less cost as compared to traditional architecture. Big data analytics have seeped into the BFSI sector and is now transforming the way companies are run. The scope and use of big data are far-reaching, owing to recent cloud improvements and additional innovations. With the help of Big Data technologies performance of processing portfolio scrubs has improved to 10X times from earlier. This accelerates the performance improvement of the online inquiries as the load of Offline and Online processing has been segregated. Automated Big Data systems will monitor and store as much information about the customers as possible, allowing for the most accurate and customized customer experience. It is no secret that Big Data is leading the way for digital transformation. The enhanced reliability and authenticity factors of AI and ML learning tools when combined with big data, guide companies to understand the data they generate every second without any false positives. 

Big Data is also playing a vital role when it comes to improving the predictive power of the risk models in the BFSI sector, which is why it is gaining traction in the banking industry. Big Data technology creates growth opportunities on Cross Sell and Up sell based on customer insights and current customer behavior. Some of the major implications of big data to obtain risk intelligence are fraud and credit management, market and commercial loans, operational risks, and integrated risk management. Big Data can precisely map demographics, individual preferences and financial tendencies that help risk projection and customer relationship management.

AI, ML and Big Data are not just the topic of conversations of Healthcare, Education, Marketing, Retail and E-Commerce. They have now established their stance in the financial sector, and they are here to stay. Companies are now searching for new ways to not only organize their data and insights better but also to use them to monitor the actions of their customers. Using Big Data, the customer can gain better market insights as the data is getting refreshed faster and the opportunity to view market insight on trend data becomes easier to make more accurate decisions that remove risk and bolster the bottom line. Machine learning, deep learning, and conversational AI apps are expected to propel company sales to new heights in the next 4 to 5 years. Investment in these technologies now would reap significant benefits in the coming future.

Authored by Pinkes Ambavat, CTO and CISO, CRIF India

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