Today, technology is the core of every task on even a minute level. This shifts technology’s position from being only an enabler to a disruptor of traditional business models. There are several new technologies on the block which are proving to be the cornerstones of tomorrow’s banking. Such digital transformation technologies include Artificial Intelligence and Machine Learning, which are catching up in a big way, particularly in the space of banking and finance. As per a PwC FinTech Trends Report (India) 2017, global investment in Artificial Intelligence applications reached $5.1 billion, up from $4.0 billion in 2015.
In fact, they are viewed cornerstones of a Digital India. AI tools can provide the flexibility and scalability needed for enabling digital transformation for enterprises in the banking, finance, and fintech space. No wonder, the banking and financial sector is leveraging the digital to improve customer experience as well as bottom lines. However, transformational strategies need to be molded around technology benefits in order to leverage maximum profitability.
If one thinks of the banking sector, there has been an increasing adoption of digitally disruptive technologies as compared to the other sectors. This is despite the fact that traditionally, banking has been a manpower-intensive sector, with operations that require human involvement. Yet, technology is steadily seeping into the operations and thus efficiently cutting down redundant tasks. This makes operations hassle-free and impactful as well as create a leaner system to work on. Cloud computing, mobile-first, and digital dashboards have already started becoming the norm. let’s find out how new technologies such as artificial intelligence are being adopted.
“The biggest use case of Artificial Intelligence and Machine Learning, which everybody is focusing on, is in the underwriting process for loans. The process is chaotic currently. Humanly, it is not possible to underwrite loans for Rs. 50,000. Organizations are working to create the best value. Several banks and fintechs are at several stages of designing the processes. The second use case is fraud management,” says Sameer Singh Jaini, CEO and Founder of The Digital Fifth.
At the core of the banking business model is to make every customer interaction digitally enabled, whether it is account opening via a tab or other functions. AI tools and chatbots are already being deployed by several leading players to enhance customer experience. There is no paper involved and no filling of forms. The benefits are myriad. The customer spends less time along with the fact that there is no rework at the back end. Plus, there is no data leakage or loss. There are AI/ML tools these days that will crunch all the data backend and provide you with insights into customer acquisition, the most capital intensive part in the financial world. For example, if you placed a request for a check book at a branch, it would capture the data. Even if you went on Internet banking the next moment, it would track the request, since they are all interconnected. There is visibility and the customer feels in command of his transactions.
No wonder, banks are already leveraging such tools in the area of customer targeting and how to design marketing strategies. Basic clustering is manual in nature currently. It basically classifies products category wise. For example, it will slot products for Category A, B, or C. However, all that is changing now.
“Currently, processes that were earlier getting done manually are now being done through AI/ML tools, including processes such as robotic process automation (RPA). Intelligent automation is where the greatest activity is and this trend should continue. Data Analytics can be optimally leveraged. The technology is there but practitioners don’t know enough about it. Within 3 years, these technologies should get deployed,” says Jaini, who has held top positions in several banking organizations. He was the CTO at DCB Bank along with other key positions at Wipro and Infosys.
With the coming of AI/ML tools, there is a lot of apprehension over jobs losses as these technologies go about digitally disrupting industries and sectors.
“In larger entities, we should expect 30-50% of headcount decrease. A number of processes can be automated, chopping off the mundane roles. Banks will shrink. Most transactions will be instantaneous,” adds Jaini.
Machine learning tools are providing insights into how to approach a customer or how to elicit a favorable response by tweaking the existing processes. The best part is that this wave is favorable for startups and those wanting to start out afresh as they are not burdened with legacy systems and infrastructure. There are a lot of use cases of such technologies that the industry can take advantage of.