AI in Banking & Finance: Enabling Effective Business Transformations

AI in banking finance bfsi

Artificial intelligence (AI) has been the center of every discussion in the finance and banking industry over the past few years. Due to its transformative capabilities, AI can help banks and financial institutions improve their operations and deliver better customer experiences. AI-powered technological advancements like machine learning, computer vision, and NLP can also drive significant changes in the way banking and financial institutions do business. AI helps financial organizations in identifying fraud and other unusual transactions, improving customer service, and making decisions related to creditworthiness.

The success of the financial services industry depends on its ability to use technology to create new and personalized products and services. According to a report by the Organization for Economic Co-operation and Development (OECD), the global spending on AI is expected to grow from $50 billion in 2020 to more than $110 billion by 2024. And the major drivers to bring in such AI disruption in this industry include:

Big data

The explosion of the Big Data market has had a huge impact on the banking industry. Due to the rise of digital platforms and the increasing number of transactions, banks are now collecting large volumes of unstructured data, such as emails, voice messages, and images. These data are then used to improve the customer experience and provide relevant and timely information. With the help of big data, banks can offer more personalized services to their customers. By analyzing big data, the BFSI industry can now gain a 360-degree view of their customers’ interaction with their and competitors’ brands and make informed and data-driven decisions.

Infrastructure

The explosion of cloud technology along with the availability of high computational resources and infrastructure (like computers, hardware, and software) has opened up new opportunities for organizations to process large data sets more efficiently and at lower costs, thus emphasizing the organization’s AI-readiness and digital transformation.

Competition

Due to the increasing number of FinTech companies and the need to provide the best possible services to their clients, banks and financial institutions are constantly competing with each other by adopting cutting-edge technologies that harness large amounts of data they possess. As a result, they are turning to AI to improve their offerings and provide a more personalized experience for their customers.

These factors are constantly evolving. The rapid emergence and evolution of AI and the increasing number of its applications are driving the need for banks to step up their digital transformation efforts. The BFSI market is well-positioned to capitalize on these opportunities.

Applications of AI in Banking and Finance

AI is already being used in various areas of the BFSI industry. Some of these include:

Chatbots

By leveraging natural language processing (NLP), AI-powered chatbots (conversational interfaces) now interact with their customers and provide them with more personalized experiences. These chatbots or virtual assistants can also help customers open new accounts, resolve their complaints by directing them to the appropriate units, provide 24/7support, and enhance online conversations, among others.

Fraud detection

Until recently, banks relied on a rule-based approach to monitor and report suspicious transactions. This method typically resulted in a high number of false positives. Due to the increasing number of fraud-related crimes and the changing nature of the data, banks are implementing AI components to improve their fraud detection capabilities. These components can identify previously undetected transactions and data anomalies. Instead of relying on a rule-based approach, banks are now using AI components to proactively detect fraud to prevent it before it happens.

Investment valuation

The investment valuation process for financial firms involves many complex calculations and collaborations among various teams within an investment firm. The teams are tasked with coming up with a strategy that fits their clients’ needs while considering various factors such as diversification and timing. AI processes large amounts of data from multiple sources in real-time while learning the biases and preferences of the individual analysts (regarding investments, risk tolerance, time horizon, etc) and determining the best investment options based on fundamental and technical data.

Predictive analytics

Machine learning and AI have enabled accurate predictions and forecasting. Data analytics and AI are being utilized in various applications such as revenue forecasting, stock price predictions, risk management, and many more. The increasing amount of data collected has resulted in a gradual decline in the number of human interventions required to improve the models.

Transaction data enrichment

Artificial intelligence and machine learning are used to decipher unintelligible strings of characters that represent transactions and merchants and convert them to readable texts to draw insights about the transaction, customer, and merchant. This method helps financial institutions and their customers understand the details of their transactions. The information provided by this method can help the BFSI industry reduce its customer service calls and fraud research costs. The descriptions of financial data help developers put the data into context so they can easily analyze and make informed decisions about their purchases. This can also help them improve their credit score and budgeting.

There are many other ways to apply AI in the banking and finance industry. However, the vast amount of opportunities for the application of AI in this sector brings in a few challenges too.

  • The lack of understanding of how AI works brings up the need for various regulatory and governance frameworks to implement AI and ensure that the users are not subjected to discrimination or bias. Data bias that results in unfair discrimination will be unethical to the financial goals and objectives of banks and financial institutions. Hence, to overcome this challenge, explainable AI is gaining momentum to ensure human oversight and judgment.
  • Despite the continuous learning of AI models, they can still suffer from tail risk due to the emergence of epidemic events, like COVID-19, that affect the learnings of ML models. Unforeseen circumstances can prevent data from being captured and used by AI models accurately. This issue can affect the accuracy of their predictions and render them unusable. Thus, despite the technological advancements that AI has made, it still requires a human in the loop to perform its various tasks.

Financial institutions have been continuously investing in technology to improve their operations and provide better services to their customers. With the right implementation, they can reduce risk and improve their human decision-making capabilities. AI-powered solutions can help financial institutions remain competitive in the market by reducing their operating costs and improving their customer support.

Do contact us to learn how we can help you embrace AI in your financial operations. We help financial institutions implement and manage high-impact use cases using AI, machine learning, and cloud technologies. We also help our clients in their digital transformation journey with our innovative and technology-rich solutions. To know more, visit our website.