AN IIT Bombay alumnus, Alok has been associated with Visionet for over five years now, prior to which he has worked with companies such as Reliance Industries, XL Dynamics, Centrix Techology, and Altisource Business Solutions, to name few.

Times are changing, and so is the way the customer banks today. As the global banking industry becomes more service-oriented, banks have responded by adding digital transformation to their arsenal. The biggest challenge is to transform the system without disrupting the existing system digitally. Artificial Intelligence and Machine Learning are the two tools that enable a risk-free transition into automation and system modernization. Technology can make it possible to achieve operating processes, risk management, and better governance. In a survey from Deloitte in 2018 that was published by the World Economic Forum, 76 percent of the CEOs have opined that AI should be the top priority for taking the banking sector to the next level.

AI & ML in Banking Industry
AI can be used to segment payments, suggest services to customers taking into account the past payment history, and help answer common operational queries via ‘bots’. Customers can get real-time solutions to problems and manage their finances.

Mitigating Risk management
The bank can use machine learning algorithms to mitigate risk in the loans department. The past system of judging the creditworthiness of a loan seeking customer had flaws. The process was neither accurate nor seamless. There were challenges in ascertaining the customer’s creditworthiness, and banks would often misjudge and end-up with bad loans. The modern technology involving AI and ML has algorithms that can analyze the client’s potential towards loan repayment. The bank can build a repository of healthy loans with utmost security.

AI and ML can help banks understand the customer’s needs that are not even apparent to the customer himself/herself. Most banks are still deploying legacy systems. It is not an easy task to implement complex transactions beyond more direct deposits and transfers. AI can help banks leverage data already present in their approaches to personalize customer life cycles and experiences. The bankers will gain meaningful insights into customer behavior and provide a smooth customer journey by manipulating data to suit customer requirements. AI and ML can offer real-time recommendations to improve customer experience and create customer delight. It helps to enhance transactions that translate into profits for the bank. Predictive data analysis can be the basis for a win-win proposition for both banks and customers alike. AI can be instrumental in improving compliance and improving operational and administrative efficiencies.

Reducing fraud is also a significant benefit that AI and ML can provide to the banking system. With the increase in sophisticated defaulting and phishing crimes, Digitalization can reduce or even eliminate the alike. Banking regulations are strict and have to follow very stringent government guidelines. Thus, it becomes integral to a robust banking system that is aware of the risk long before it appears. The fraudulent or suspicious activity must be apparent to the banker before it is apparent and affects the customer’s account. Machines using AI and ML can detect the phenomenon by performing intensive and extensive real time analysis, which is impossible to emulate by human brains.

Digitalization must be adopted by the global banking systems as soon as possible, as international banking has genuinely made this world a financially connected place. Data security and safe transfers are the day’s norms, as billions of dollars are processed every day on a global level. There must be no breaches, and transactions must be smooth and seamless.

Digitalization Must Be Adopted By The Global Banking Systems As Soon As Possible, As International Banking Has Genuinely Made This World A Financially Connected Place

Omnilevel Decisioning as a Part of the Digitalization Transformative Drive
Customer engagement is essential to every industry, but more so in the banking sector. Banks require a fully transparent 360-degree visibility parameter that understands the customer’s banking habits and unique requirements. Data from all avenues like apps, ATMs, APIs, bank counters, and third party applications must be integrated to get the larger picture, and an Omni-level outlook betters the decision-making process. Data is mined from various sources and integrated and analyzed to create an actionable platform for predictive analysis. AI can then be used effectively to create real-time recommendations that are highly personalized. The personal problem-solving activity will lead to customer loyalty, value, retention, and sustainability.

Real-time transaction analysis that is critical to collating data empowers banks. AI and deep learning can add value to the customer experience by predictive counseling. The customer is presented with multiple choices that are based on his/her preferences and needs. The banks can curate packages that have a bouquet of products and services that the customer needs.

While innovations are important, improving and optimizing processes hold an important part as well. Banks can expedite the workflow, solve bottlenecks in the process, reduce call center load, and still improve customer service. With the help of smart technology and chat-bots, performance can be automated and augmented, and this frees-up the banker to improve productivity and drive more customers, armed with real-time data. AI and ML provide predictive data analysis which creates patterned data sets of customers’ saving and spending behavior, and this can give banks a competitive edge and a better user experience.