By Vipin Vindal, CEO, Quarks Technosoft
In an era where technology influences the fate of businesses, Generative artificial intelligence (AI) has emerged as a game changer, creating an enduring impression on industries and leaders worldwide. From igniting amazement to sparking interest, its influence impacts everyone. Just like other sectors, the transformative power of gen AI is reshaping the financial sector, unlocking new levels of accuracy, efficiency and insights. From data analysis and reporting to procurement, Gen AI can automate and streamline a wide range of financial processes. This revolutionary wave is more than just enhancing existing processes; it is about forging new paths and developing smarter, more efficient and highly personalised financial solutions, significantly benefitting both operational efficiency and customer experiences.
What led to the change?
During the pandemic period, the banking and financial services sector (BFSI) faced numerous issues, including an increase in cybercrime and fraud, shifting client expectations and changing global macroeconomics. As a result, banks and other financial institutions (FI) were compelled to find solutions and rewrite business models to drive customer satisfaction and combat crimes while following the regulatory mandates.
Among the conventional approaches, the productivity-focused model including AI and machine learning has been widely adopted. Being integral to the BFSI sector for years, AI, replaced humans with chatbots to help customers with their queries and even improved laborious processes such as document underwriting and fraud management that are based on existing data. However, these systems are designed for structured data and cannot manage and extract insights from unstructured data. Consequently, this led to the inclusion of Gen AI in these automated procedures.
GenAI: The new normal
Generative AI can learn from various data sets, identify patterns and trends and make educated judgements based on the findings of its investigations. Its capacity to produce synthetic data that closely resembles real-time data for training machine learning models helps improve pattern recognition and prediction. As a result, its applications in the BFSI sector are immense in comparison with conventional AI models, including not just automation and increased efficiency, but also sophisticated fraud and risk management, portfolio management and the production of new income streams.
According to Market Research, gen AI in the global finance market is anticipated to reach USD 27,430.7 million by 2033, owing to its potential benefits. Let’s now discuss those benefits with a broader perspective:
Risk management: Financial institutions have to frequently counter with credit, fraud and market risks. Generative AI can generate data sets that replicate various market scenarios that can help both banks and FIs build effective risk management strategies. Its capacity to detect patterns and abnormalities in massive volumes of data can assist them in preventing financial crimes and reducing risks associated with loans and investments.
Improved customer experiences: In today’s time, customers want experiences that are personalised to their individual demands, like increased accessibility, faster processing times, and specialised goods and solutions. For instance, a virtual assistant that can not only effectively answer every client inquiry in real-time, but also offer product suggestions based on the customer’s information. Moreover, gen AI can analyse client data to provide personalised financial solutions, improve operations and offer hyper-personalised experiences that increase customer pleasure and build long-term loyalty.
Streamlined operations and regulatory compliance: Cyberattacks continue to pose a danger to the BFSI sector. Also, regulatory demands are continually evolving. This, in, turn has made it difficult to manually analyse the massive volumes of heterogeneous data that these organisations deal with on a daily basis, which is both time-intensive and error-prone. Generative AI, in these cases, can automate repetitive processes like ‘Know Your Customer’ (KYC) campaigns, loan application processing and report generation, allowing people to focus on more strategic duties. Additionally, it can be used to track regulatory shifts and compliance demands that can be addressed predictably, assisting banks and FIs in saving time and money.
Data-focused decision making: There is a vast generation of data in the banking and financial services industry. This data consists of patterns and trends that may help influence lending, investment and overall strategy choices while facilitating successful cross-selling, up-selling as well as the production of additional income streams. With deeper insights and predictive analytics enabled by generative, banks can make data-driven choices enhancing company operations, performance and customer satisfaction.
In conclusion, the integration of generative AI into the banking industry is not a fad, but rather a fundamental shift towards more intelligent, efficient and personalised financial services. Despite certain hurdles, the potential benefits of navigating this landscape are significant. As the financial industry evolves with AI, companies are on the verge of a future in which finance is made more accessible, transparent and suited to individual needs than ever before. Furthermore, the road ahead is full of potential for innovation and reinvention, suggesting a brighter, more inclusive tomorrow for financial services globally.
