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Role Of AI In Fintech Development: Top Use Cases

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Explore the transformative role of AI in fintech development and top use cases. Learn how AI is reshaping the financial industry.

Chirag Pipaliya

August 21, 2024

According to Globe News Wire, artificial intelligence is expected to be valued at an astounding 49 billion dollars by 2028. Fintech AI is poised to undergo an unparalleled technical breakthrough. Artificial Intelligence (AI) in Fintech organizations is revolutionizing customer service, simplifying payment procedures, and augmenting the effectiveness and expansion of financial services. It has evolved from a futuristic concept to a fundamental component of innovation.

The move toward digitization highlights the significance of artificial intelligence (AI) technologies, such as big language models and generative AI, in the banking industry and identifies them as the main forces behind a revolutionary period in finance.As a leading fintech software development company, we at Vasundhara Infotech are driving these advancements with cutting-edge web solutions.

The Role of AI in Financial Sector

Understanding how AI is utilized in FinTech indicates how deeply it is integrated into the fundamental operations of financial services, increasing efficiency and driving AI in financial services revenue. Within this dynamic context, AI's ability to transform client engagement with financial services is critical. To demonstrate the substantial impact of Artificial Intelligence on financial service providers, we observe widespread adoption of numerous technologies aimed at improving the client experience, lowering costs, and increasing overall operational efficiency.

AI-powered client-centric solutions, such as tailored financial advice and AI assistants, offer continuous and efficient consumer engagements. The role of AI in financial firms goes beyond convenience, ensuring that customer involvement is both cautious and meaningful. This technology breakthrough enables financial services to address the changing needs of its consumers, ensuring customer loyalty and providing a competitive advantage in a congested field.

Also read: How To Build An AI Software: A Comprehensive Guide

Top Use Cases of AI in Fintech Development

The following use cases illustrate the suitability of fintech AI, ranging from enhancing internal procedures to positively impacting customer satisfaction. 

  • Customer service chatbots

60% of consumers would select the chatbot over a human customer agent if they were presented with the option of waiting for 15 minutes. Time is of the essence. Fintech is an industry in which customers may have numerous inquiries due to individuals' significant concern regarding managing their fortune and assets. 

Chatbots have the potential to reduce customer service congestion and offer more rapid insights than a one-on-one customer service agent. 

  • Predicting customer churn for financial apps 

Historic customer churn rates can be determined using a straightforward equation; however, comprehending the ongoing obstacles is exceedingly intricate. Predictive AI bridges the gap between the causes of customer churn and the most probable causes by utilizing events such as logins and search behavior as indicators. 

Additionally, predictive AI can evaluate the likelihood of a customer's churn, enabling the remediation of a strained relationship before the customer's departure. Rather than passively observing customers depart, predictive AI can target them proactively with personalized campaigns and more genuine support to rekindle their interests before they are irretrievably lost.

  • Robo-advisors 

AI trading assistants have relocated investors from the congested trading floors to the real world, where they can execute high-stakes transactions with greater assurance and from the comfort of a mobile application. Traders have entrusted billions of assets to this time-saving technology since the first robo-advisors entered the market in 2008. These will receive $1.8 trillion in investments from global investors in 2024 alone.

This technology does not merely mitigate the likelihood of human error. Robo-advising technology provides traders and investors with access to an immense volume of trading data that is impossible to monitor manually. The statistical models of AI have also advanced significantly. 

The training data used to predict future outcomes is derived from past trading victories, and machine learning (ML) is integrated into it.

  • Fraud detection and prevention 

Developing algorithms to analyze extensive financial data is not a novel phenomenon. For years, the IRS and other government entities have implemented this measure, which has had a substantial effect on reducing fraud.

In light of the proliferation of digital money schemes and fraudulent insurance claims, private companies are obligated and allowed to implement comparable measures. AI may be the ideal solution to a fraud epidemic, as it is impossible to manually evaluate every transaction that transits through a bank or money transfer service. It can immediately identify fundamental spending patterns for an individual consumer and compare them to real-time transaction data. 

Subsequently, it will indicate any discrepancies for an employee to review. AI is also perpetually awake, which can be a significant advantage in the fight against global crimes in a digital environment that operates around the clock.hich can be a real benefit for combating global crimes in a 24/7 digital environment.

Also read: How AI is Transforming E-commerce Website Development

  • Financial forecasting

Cash-flow forecasting becomes less overwhelming with AI, allowing financial professionals to concentrate on the correct trends at the right times. Moreover, it highlights erroneous data that may compromise prediction accuracy. Today's AI projections use both user data from the organization and data from outside sources. 

In fact, artificial intelligence (AI) can help make better financial projections and gradually lower risk by analyzing a wide range of factors, including the weather, commodity prices, international political forces, and past consumer habits. 

  • Build predictive models faster with GenAI 

82% of the companies that employ GenAI do so because they believe it will "significantly change or transform" their industries. Fintechs benefit from the use of large language models (LLMs) such as ChatGPT4, which not only facilitate the development of new products and services but also expedite the ideation process.

GenAI is highly regarded as a coding assistant and can assist your data team in refining existing machine-learning models and developing new ones. In the absence of the most recent GenAI tools, data analysts are required to conduct extensive research for days or weeks before developing a new model from the ground up. 

Predictive GenAI is capable of generating code for your models in mere minutes; it merely requires prompting.

  • Credit scoring and risk assessment/loan underwriting 

In the past, the approval of a credit card or auto loan was contingent upon a credit report, income, and a few other standard pieces of information. Although this qualifies the most likely consumers to repay a loan, it excludes certain borrowers who are likely to be able to pay, thereby leaving business on the table.

In addition to the credit score at the precise instant of the credit pull, new AI-based credit scoring models surpass the capabilities of a static application that banks encounter. These models are capable of predicting with greater precision whether an individual is a genuine credit risk by analyzing historical data from a diverse array of public and private sources. 

This allows for the inclusion of additional borrowers who have been previously overlooked, and it prevents banks from lending money to applicants who appear to be good on paper but are extremely likely to default. 

Conclusion 

The integration of fintech AI is driving significant advancements across various aspects of the financial industry. From enhancing fraud detection to delivering personalized financial advice and improving regulatory compliance, AI is enabling financial institutions to operate more efficiently and effectively. As technology continues to evolve, we can expect even more innovative applications of AI in fintech, further transforming the way we manage and interact with our finances.

The future of fintech is undoubtedly intertwined with AI, and those who embrace and leverage these advancements will be well-positioned to thrive in an increasingly digital and data-driven world. We at Vasundhara Infotech specialize in providing top-notch fintech software development services that align with your goals. 

Contact us today to discuss your requirements.




FAQ's by Role Of AI In Fintech Development

AI enhances fintech by automating processes, improving decision-making, and providing personalized services. It helps in fraud detection, risk management, and customer service.

AI algorithms analyze transaction patterns and identify anomalies in real-time, reducing the chances of fraud and enhancing security.

Yes, AI can analyze a variety of data points beyond traditional credit scores to assess creditworthiness, offering more accurate and inclusive assessments.

AI-powered chatbots and virtual assistants handle customer inquiries, provide account information, and resolve issues 24/7, improving user experience and efficiency.

Yes, AI can provide tailored financial advice and investment strategies based on individual goals, spending habits, and financial conditions.

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Chirag Pipaliya

As the founder and CEO of Vasundhara Infotech, he leads with a blend of vision and strategy. His entrepreneurial spirit drives growth and innovation, shaping the company's success story.

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