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Fintech uses AI and ML for underwriting to boost  financial inclusion of startups

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The fintech market in India is expected to reach $150 billion by 2025, growing at a CAGR of 20%. This has transformed financial services, especially the way startups secure funding. The underwriting process is traditionally faced with a myriad of challenges, such as manual data handling, a lack of comprehensive data sources, and assessing risk subjectively. These challenges often lead to slow, ineffective, and unreliable appraisals, which hinder timely access to funds needed by start-ups.

To solve these problems, fintech innovations have come up. The use of AI (artificial intelligence) technology and ML (machine learning) in underwriting is revolutionising the game completely. According to a report by the National Association of Software and Service Companies (NASSCOM), AI and data analytics in India are expected to add $450–$500 billion to the GDP by 2025. By doing so, these technologies enhance efficiency in the collection and organisation of data while improving accuracy and speed during financial assessment, thereby increasing the possibility of more start-ups penetrating into the market.

The first step towards transforming modern underwriting involves changing how data is obtained and managed. Conventional approaches were heavily dependent on manually fed-in information and fragmented records, thereby causing inefficiencies as well as possible inaccuracies. Conversely, current financial technology solutions utilise automation techniques to enable the fusion of different types of information sources.

Various sources of data, such as bank transactions, financial statements, credit scores, social media activity, and even customer reviews, can provide a huge amount of information to advanced data management systems. These systems are equipped with very powerful algorithms that make sure that the data is accurate and relevant, which helps reduce the time and effort needed for manual inputting and verification of data. This ensures not only faster underwriting but also a more comprehensive view of the start-up’s financial health.

After the collection and organisation of data, the next important thing is evaluating it. This is where AI (artificial intelligence) and ML (machine learning) come in handy. The AI-based tools study gathered data sets; they identify patterns and trends that human analysts may overlook. These tools learn through past records about company runway, profit margins, debt exposure, customer acquisition costs, revenue growth rates, etc.

The machine learning models keep on improving themselves by learning from previous company performances as well as market conditions. For instance, there could be further analysis required for a startup with high revenue growth but high customer acquisition costs. The ML model should predict if these high acquisition costs will decrease over time or whether they indicate a long-term issue affecting the stability of company finances.

The application of AI and ML in data evaluation has multiple advantages. According to a report by NITI Aayog, AI adoption in India can boost its annual growth rate by 1.3% by 2035 and add $957 billion to the economy by 2035. To start with, it improves financial evaluations by excluding human prejudice and mistakes. Furthermore, it makes underwriting faster, which is essential for quick decision-making. Lastly, it leads to a more comprehensive understanding of the risk profile of a start-up, thereby allowing a closer fitting of financial solutions for them.

Risk assessment is very vital in underwriting because it determines whether a start-up will qualify for a loan and under what terms. Risk assessment methods were often based on limited information and subjective judgements, making decisions that were either too conservative or excessively risky. AI-driven risk assessments are, however, turning around this process by providing data-driven objective analysis of risk factors associated with the company.

AI models assess countless variables, such as trends in the industry, historical financial performance, market conditions, and even macroeconomic indicators. By studying these aspects, AI can better predict forthcoming financial outcomes. For instance, a company operating in an expanding sector with a sound consumer base as well as strong profit margins could be seen as having lower risk despite short-term fiscal challenges.

Moreover, AI-driven risk assessment can allow for more flexible and dynamic loan structures. Instead of using a one-size-fits-all approach, fintech companies may develop loan products that match a startup’s specific needs and its risk profile. This might involve lowering interest rates for low-risk startups or tailoring repayment schedules based on projected cash flows.

The advent of artificial intelligence (AI) and machine learning (ML) in underwriting processes is much more than an efficiency increase and accuracy improvement; it is a complete makeover of the financial evaluation landscape. Such advanced technologies are gradually taking the place of underwriting techniques limited by their own imperfections, thereby causing great turmoil in the sector.

Such access to and democratisation of capital cannot be underestimated, as it has changed the most. Startups often find it challenging to secure financing via traditional means, particularly those operating in underserved markets or with unconventional business models. Being able to carry out a more comprehensive and precise risk assessment, AI and ML enable these start-ups to tap into finance that would otherwise be inaccessible.

Furthermore, with AI-driven underwriting, default rates can be brought down significantly. By accurately appraising risks and tailoring financial solutions to the unique circumstances of each start-up, fintechs can reduce the chances of default. Artificial intelligence-supported underwriting has been shown to reduce default rates by as much as 25%, an indication that it may strengthen financial stability.

The other significant impact is the speed at which funds are provided. Underwriting can take weeks, if not months, resulting in delayed access to important capital. This process, on the other hand, has been made quicker by AI and ML, whereby decisions can be made in days, if not hours. For example, this quickness is very essential for startups that require them to move fast when there are market opportunities or immediate financial matters.

Moreover, this enhances innovation within the fintech industry itself through the application of machine learning and artificial intelligence in underwriting. As these technologies continue to advance, they open up new possibilities for creating more complex financial products and services. Predictive analytics, for instance, can be used in designing dynamic loan products that adjust terms depending on real-time financial performance, thus giving startups the flexibility and support they need even more.

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