Harnessing the Power of AI to Democratize the Financial Landscape

The convergence of artificial intelligence tools and technologies in the BFSI segment, particularly the fintech space, has radically changed how financial firms undertake operations compared to barely a few years ago. Be it lending, investing, insurance, customer service, fraud detection, and more, AI has many use cases in the financial realm. 

The impact of AI on the fintech market is clear from the projected growth rates – as per a Mordor Intelligence report, it is anticipated to rise from $44.08 billion to $50.87 billion between 2024 and 2029.  Moreover, a Goldman Sachs Research report reveals that with the use of generative AI in finance, there will be a potential increase of 7% in the global GDP while universal productivity may rise marginally by 1.5%.  

Speed, Efficiency, and Effectiveness

The revolutionary capabilities of AI have transformed the efficiency and effectiveness of financial entities, allowing them to deliver swift data-driven offerings alongside personalized services. Earlier, whether seeking loans or investing in stocks, the decisions were based on the human element, which was influenced by factors such as personal preferences, market trends, current conditions, etc. 

Given the shortcomings of the human intellect, however, it’s difficult to garner and assess immense amounts of data at short notice. Conversely, AI algorithms can do this at exponential speeds, offering pointed insights along with risk assessment and probable outcomes. AI offers similar insights in the case of portfolio management. AI empowers financial advisors to implement well-calibrated decisions that optimize customers’ investment portfolios while pinpointing potential risks and predicting market trends and fluctuations. 

To be sure, it isn’t just back-end operations that have been revolutionized. AI is also revolutionizing how financial service professionals interact with customers. Consider personalized banking. Backed by the lightning power of machine learning (ML) algorithms, banks, and other financial players can customize products to suit individual requirements and preferences. Thanks to offerings that resonate with consumer requirements, BFSI companies can ensure greater customer satisfaction. 

Benefits for Underwriters and Lenders

Banks and other lenders also benefit from more judicious decision-making through AI. Like insurers, lenders make financial decisions based on barely 10% of the available data because it is exorbitantly expensive or extremely challenging to access the requisite data. By overcoming these limitations, AI enables lenders to make truly well-informed decisions. Let us understand how AI does this. 

One of the main barriers loan underwriters face is the intensive process of collating, authenticating and analysing huge quantities of borrower data. Besides being time-consuming, this manual method leaves lenders vulnerable to the risk of overlooking key details or even misjudging risk factors. 

Given its technological prowess, AI uses ML algorithms to process massive sets of data quickly and accurately. By integrating AI in loan underwriting, automation manages mundane work, whether it is data collection or risk assessment, substantially crunching the time and effort required for this. As AI-enabled underwriting systems are constantly learning from historical data and adapting to evolving market conditions, it enhances its predictive accuracy. In this way, financial entities can make more timely and well-informed decisions. 

What’s more, modern lenders are shunning traditional practices to evaluate the creditworthiness of customers. Instead, they are using alternative scoring models powered by AI. This transformative system assesses credit scores efficiently against predetermined norms by utilizing credit bureau data of customers alongside AI-linked credit scoring to discover eligibility.   Eligible customers are provided with personalized credit offers. If accepted, the seamless process continues via automated KYC and the digital onboarding of clients. 

AI integration not only fast-forwards credit assessment but also drives swift loan disbursal, facilitating faster, more responsive lending decisions. With AI-powered protocols, customers can expect efficient credit assessments and speedy disbursals to meet urgent needs. 

Proactively Managing and Mitigating Cyber Threats

The crucial role of AI in risk management and mitigation must also be mentioned. With non-stop analysis and monitoring of data for prospective threats, AI algorithms identify specific patterns that indicate market anomalies or suspicious activities. AI-enabled tools allow financial players to validate transactions, augment security, and counter threats from cybercriminals. By leveraging AI, companies can monitor countless e-payment and credit card transactions daily, discover changes in purchase patterns or transaction behavior in real-time and offer a robust, streamlined process to stop any fraudulent activities and safeguard customers. 

As a result, financial firms can reduce or prevent fraud at various levels, including phishing and money laundering activities. The proactive risk-mitigation approach protects the interests of both financial institutions and customers, fostering greater trust in the integrity of the financial ecosystem. 

Furthermore, these processes and transactions are undertaken speedily unlike human actions that need more time. This applies to a range of activities such as customer onboarding, loan processing, checking deposits, withdrawing funds, etc. 

Tools to Democratise Financial Services

Financial services are also being democratized through the advent of robo-advisors and digital wallets, to mention just two innovations. Chatbots and other generative AI tools are transforming customer service by ensuring immediate support and guidance. Besides being highly efficient and personalized, virtual assistants are adept at learning from every transaction, and thereafter anticipating the needs of customers. 

These advantages mean people no longer need to visit physical branches. Consequently, it has made financial services accessible to previously underserved or unserved communities across the country, empowering even common people to take charge of their finances.  

Counting on Collaboration

Notwithstanding these benefits, a collaborative model between humans and AI is best to obtain more productive outcomes. For example, credit underwriting driven purely by AI algorithms can run the risk of prioritizing profits without understanding the social implications. Gradually, the algorithm could begin discriminating against specific population cohorts because of AI’s inclination for self-learning based on its past decisions. 

Keeping these elements in mind, artificial intelligence managed by human intellect and oversight is best to advance the government’s mission of inclusive development even as AI redraws the contours of the financial world by streamlining workflows and providing real-time solutions. 

Authored by Guruprasad Baskaran, Head of Engineering, mPokket