The important role that fintech companies play in empowering small business owners in India is evident from the wide range of services they are offering– from payments, access to credit and investment options, to better inventory management, better financial management, amongst others, enabling them to access working capital and cash flow financing. As more and more businesses are getting digitized, fintech companies are enabling a wide range of businesses and individuals to flourish in the digital era by making all financial services available in one place, without any dependency on physical infrastructure. AI and ML are indeed redefining financial services by enabling better underwriting, a superlative customer experience, and personalized offerings, amongst others.

Rise of Gen AI 

AI has seen several breakthroughs and innovations over the last few years and Gen AI is one of them. GenAI is an offshoot of Artificial Intelligence (AI)  and a specific subset of Machine Learning (ML) technology that focuses on building systems that can generate new data, such as images, videos, and audio. Large Language Models (LLMs) are at the core of Gen AI, capable of absorbing large amounts of data including text and documents, and producing content in easily usable formats. It would be right to say that GenAI has the potential to empower financial services providers and fintech companies by facilitating gains in efficiency and cost savings, reshaping client interfaces, enhancing forecasting accuracy, and improving risk management and compliance.  

Integration of Gen AI in fintech is happening at a rapid pace. As per a report by Statista, the market size of the Generative AI market is projected to reach US$0.76bn in 2023 growing at an annual growth rate (CAGR 2023-2030) of 27.66%, resulting in a market volume of US$4.20bn by 2030. The addition of Gen AI technology in fintech is only going to make its benefits more widely available.

GenAI is expected to emerge as one of the most powerful tools and differentiators for fintech companies. For example, it can help automate investment allocations based on a person’s age, and income by reading language data and identifying patterns in them and can help in delivering a better fintech experience by identifying customer experience and recommendations through Gen AI. Also, GenAI finds a lot of relevance in fraud detection and prevention as it helps in identifying suspicious activities. It generates fraud alerts and flags potentially fraudulent behaviour by analyzing historical transaction data and detecting anomalies.

There are numerous use cases of GenAI for fintech. Here are some of the top use cases of GenAI in the fintech Industry:

Risk Assessment and Credit Scoring: The role of risk assessment in fintech and financial institutions is very crucial as it plays a key role in lending decisions. Gen AI can analyze historical data market trends and other relevant factors to assess the risk, thus, improving the accuracy of risk assessments and credit scoring models leading to more accurate lending decisions and in turn, ensuring a lower delinquency rate in the long run.

Enhanced Data Augmentation: Generative AI can augment existing data sets and create synthetic data points to enhance the quality and quantity of training data. By generating synthetic data points Gen AI helps overcome limitations imposed by scarce datasets, thus improving the performance and robustness of AI models.

Personalized Financial Recommendations: By analyzing user data and risk tolerance, Gen AI can help fintech companies offer tailored product recommendations about investments, savings, and financial planning to customers

Fraud Detection and Prevention: Gen AI can analyze large volumes of data in real time and identify patterns that indicate suspicious activities and potential fraud.  With real-time fraud detection, fintech companies can apply fast detection and mitigation of fraudulent activity in real-time and prevent financial losses. 

Computer Vision Enhancement: By leveraging GenAI, fintech companies can enhance their computer vision capabilities which can transform the way visual data is analyzed. Features like automated image recognition, object detection, and facial recognition can be improved drastically with the help of Gen AI ultimately streamlining and improving security measures.

Strengthening Digital Security: Gen AI can bolster the digital infrastructure of fintech companies ensuring the integrity and confidentiality of financial data. With the help of GenAI, cybersecurity teams can automate repetitive tasks, accelerate threat detection and response, and improve the accuracy of their actions to strengthen the overall security apparatus against cyberattacks.

Automated Customer Service and Support: Gen AI-powered chatbots and virtual assistants can provide instant, 24/7 customer support, handling routine inquiries and freeing up human agents for more complex tasks.

Using Generative AI (GenAI) in fintech can ensure a host of benefits, but it also comes with its own set of challenges. The top challenges include:

• Data Security and Privacy: Generative AI’s potential to process personal data can raise concerns about data security and privacy. When personal data is fed into the generative AI platforms for training purposes, the risk of breaching privacy regulations does arise. The notion of data anonymity is challenged which could lead to breaches of user privacy particularly when the generated content includes personally identifiable information.
• Bias and Fairness: Generative AI models are susceptible to bias arising from various sources, such as training data, which can lead to unfair or discriminatory outcomes. In fintech, this can result in unfair or discriminatory outcomes, which could lead to legal and reputational risks.

To conclude, Generative AI has emerged as a game-changer in the fintech sector, particularly in the realm of fraud detection and risk assessment but a full realization of the technology’s benefits will take time, as there will be challenges starting from managing the inherent risks in generative AI to determining what new skills and capabilities the workforce will need, and rethinking core business processes such as retraining and developing new skills which will need to be addressed. Going forward, if the fintech Industry manages to address the ethical and regulatory considerations surrounding Gen AI like data security and privacy, then it is expected that this transformative technology would shape the future of the fintech industry in the times to come.

LeackStat 2024