Impact of Artificial Intelligence on the Financial Services Industry
ACTIVISM
The rise of AI is breaking the process of this industry, allowing innovations with the new operating models. AI stimulates human learning into machines, which perform work and carry out activities like humans. The machines can be trained to organize and make sense of the information and predict something according to that information. This makes AI the most required technology in the BFSI Industry, changing the marketing of the products and services.
Why AI in Banks? Why Now?
The task of the BFSI sector is to focus more on the quality of products and services. With the help of AI tools, organizations have not only improved ways of handling data and achieving customer satisfaction but also transformed, fastened, and redefined ordinary processes for their effectiveness. More than ever, banks are now aware of innovative, cost-effective solutions using AI, understanding that although significant, asset size can no longer be a standalone factor in building a successful business. Rather, the success of the BFSI companies is now gauged based on their ability to leverage technology and unlock the power of their data in creating innovative and personalized products and services.
AI Disruption Drivers in Banking?
Analysis of Data: The main reason why AI was integrated into the banking industry is because of the volume of the data and constant change in customer expectations. In addition to the traditional structured data, organizations these days collect large volumes of unstructured data: emails, text and voice messages, images, videos, and so forth through their customer service, social media platforms, and other data collection media. Banks can now leverage big data to personalize services. A 360-degree view of customer interaction with the brand is used by banking organizations, including basic personal data, transaction history, and social media interactions to make decisions.
Infrastructure: Fast computers, hardware, software, and cloud; the explosion in cloud technology, along with high computational resources, and infrastructure availability enables the quick processing of large data at lower costs and with efficiency in scalability. This implies that organizations are more than ever prepared for AI.
Regulatory Requirements: Banks are in constant focus of regulators, and to ensure timely and correct reports to meet their regulations. AI-driven solutions present an opportunity to address some of the drawbacks in contemporary financial systems: they can improve the speed and quality of the decisions made while encouraging the organizational to respond to regulatory compliances. The coming of AI further requires the renewal and deep revision of outdated regulations and the sound alteration of the existing shape of world financial markets. This is, therefore, a golden opportunity for the teams in compliance to invest strategically in technologies that will help them make their banks more future-proof.
Competition: Banks continue to compete with others in the industry and take the opportunity of already existing cutting-edge technologies to utilise the vast amount of data they possess. In return, the banks are using AI to optimize current service offerings, take new offerings to market, and provide a more personalized experience for their customers.
The above-mentioned factors are continuously changing and hence bringing new values and opportunities to the businesses, which can tap into the benefits offered by AI. The BFSI market is in an excellent position to move with this disruption and grow in its digital transformation process.
Applications of AI in Banking
Most of the banks are now taking advantage of this technology. Below is a sample of use cases where AI has been most profound with the BFSI industry:
Chatbots: Integrated AI-powered chatbots with Natural Language Processing (NLP) interact with customers 24/7 and boost online conversations. These chatbots are beyond the ordinary response to customers' inquiries in efforts to help them work through their account details; today, they are used in opening new accounts and initiating complaints to appropriate customer service units amongst others.
Fraud Detection & Prevention: Until now, banks have operated with traditional transaction monitoring and name screening systems that generate a high number of false data. With an alarming rate of increase in fraud-related crimes and evolving patterns on a day-to-day basis, new AI components are being integrated within the existing systems to support the discovery of unnoticed audit trails, anomalies in data, and suspicious people/entity relationships. This enables a proactive approach: AI preventing fraud before it takes place, as opposed to the typical inherently reactive approach of fraud detection.
Customer Relationship Management: Customer relationship management is one of the most important aspects of banking. Banks have become customer-centric and are engaged in providing services around the clock even to individual customers, such as providing facial recognition and voice command features to log into financial applications. Banks also use Artificial Intelligence technologies for the study of the different ways in which customers use its services, by studying the data from customer transactions and then segmenting the customers automatically to allow targeting marketing and hence improving the experience and interaction with the customers.
Predictive Analytics: With the advent of Machine Learning (ML) & AI, accurate forecasting and prediction are now possible and it is applied in budget and forecasting, technical analysis, and risk monitoring. The exponential rise in data availability is instrumental in making these models more and more efficient, thus gradually reducing the level of human intervention they require.
Credit Risk Management: The focus of risk management supervision by the regulators requires financial institutions to come up with more reliable models and solutions. The credit facility borrower's creditworthiness is appraised by using AI—data harnessing to predict the probability of default, which helps in improving the accuracy of credit decisions. In this regard, the market progresses to insights-driven lending, rather than expert judgment; maximizes rejection of high-risk customers and minimizes rejection of creditworthy customers; and reduces credit losses incurred by the financial institutions.