The use of AI in Finance and FinTech has enabled the sector to ramp up in the past few years. And, like other industries, AI is reshaping how the BFSI industry operates.
The global FinTech market is expected to hit US$324 billion in 2026 at a CAGR of 25.18%.
Deploying Artificial Intelligence techniques in financial service areas, such as asset management, algorithmic trading, credit underwriting, or blockchain-based finance, has facilitated the sector through its incredible computing capacity.
Just like AI in the education sector, AI is helping the financial sector to improve its efficiency through cost reduction, productivity enhancement, personalized products, and extracting data insights to suggest investment strategies.
Through this article, let’s dive deep into the current and future AI use cases in the FinTech sector.
A note on Artificial Intelligence (AI)
Artificial intelligence, or AI, is machines’ simulation of human intelligence.
These machine-based intelligent systems with varying levels of autonomy can learn from a human-defined data set and make predictions, recommendations, or decisions based on that.
Developing such advanced systems, which can make decisions similar to or better than humans, requires specialized hardware and software for writing and training machine learning algorithms.
These algorithms improve predictability and performance automatically through the live learning experience and fed data without being programmed to do so by humans.
A note on FinTech
Financial Technology, usually referred to as “FinTech,” is the new technology that aims to improve and automate the delivery or use of financial services.
At its foundation, FinTech undertakes specialized software and algorithms for computers/smartphones and better manages financial operations for stakeholders, i.e., Service providers and consumers.
With the advancement of Internet technologies, FinTech is now becoming prominent in the conventional BFSI sector. FinTech is a major driving force behind the development and use of cryptocurrencies, such as Bitcoin.
The Use cases/benefits of AI in the FinTech sector
Big Data Analysis
AI-based tools and techniques use massive amounts of alternative data sources and data analytics referred to as ‘big data. Deep analysis of commercial and transactional data of this scale is too complex for humans to manage quickly and efficiently.
AI not only help to analyze big data and find actionable insights from it but ultimately makes decision-making faster and more accurate for humans.
AI algorithms provide big data analytics in the BFSI domain for AI-based behavior prediction, API integrations, and real-time consumer insights.
Credit Risk Assessments
The BFSI sector loses millions of dollars annually by giving credit to someone who can’t repay. Credit risk assessment is one of the major use cases of AI in FinTech.
Assessing credit risk is a mathematical model based on statistical methods that consider a large amount of financial data such as income, credit history, purchase expense behavior, etc.
Predicting one’s future financial capabilities accurately is possible with the inclusion of AI in data analytics.
Financial Forecast & Asset Management
AI forecasting can provide greater accuracy than traditional financial forecasting models. AI-based algorithms learn from past data points and can predict future trends more accurately than conventional analysis approaches.
FinTech companies can now take advantage by integrating AI technology with their Apps and forecast better market conditions.
Machine Learning models can simultaneously monitor thousands of risk factors and simulate the portfolio’s performance under thousands of market/economic scenarios. The technology can enhance risk management for asset managers, large financial institutions, or retail investors operating through individual systems.
A recent product offering by Indian broker firm Zerodha is the best suitable example. Its forecasts suggest there are uses for possible P&L in a trade considering live market conditions and trends.
AI-powered hedge funds and algorithmic trading are exciting topics to explore.
Advance Safety & Security
Finding financial fraud attempts is like finding a needle in a haystack. AI-powered analytical tools can find this needle in just a fraction. AI technologies are so advanced that they can identify a threat before the fraud attempt.
As deployed AI systems take advantage of Machine Learning, they can adapt to new undiscovered cases and further enhance the detection over time.
Behavior Prediction
Think of a father whose son is just about to graduate in the next six months. And he automatically gets a personalized offer of an Educational Loan for foreign studies by an AI-powered FinTech App.
Yes, this hypothetical scenario is possible as an AI system can predict user behavior by his financial activities.
There are hundreds of possible use cases if a computational system can predict user behavior accurately. After prediction, they then suggest personalized financial services.
Examples: offerings like a personalized insurance policy, family budget plans after analyzing their spending habits, customized home loans for a location, etc., are possible because of AI. Opportunities are unlimited.
Automated Marketing & Customer Service
FinTech companies need to follow up on various customer interaction channels continuously. Their customer service channels are expected to be safe and secure and should provide instant solutions for the best otherwise; a customer may lose their hard-earned money.
Speech AI solutions have been catering to these needs for the past few years and automate sales, marketing, and customer service channels.
For example, Companies like CellStrat Hub have developed tailor-made Speech AI tools like AI Voice Assistants, Speech-to-Text, Text to Speech etc. These tools are easy to integrate with CRMs or sales, marketing, or customer service channels.
Financial service providers are adopting these tools to lower costs, automate customer interaction channels, improve engagement and reduce friction in the customer’s financial journey.
Speech AI Tools like These AI-powered tools easily:
● Automate Inbound calls or collection calls
● Qualify sales leads and automate the sales funnel based on customer interaction.
● Send personalized renewal and reminders notifications
● Create tickets for complaints and solve first-level queries
● Make automated personalized calls for payment communications
Major Roadblocks, Challenges & Risks associated.
As AI/ML technology has a wide spectrum of applications in the financial sector, several roadblocks come in the way of developers.
As the technology is still new for many, the Lack of resources for the developers & researchers is the industry’s major challenge.
CellStart Hub is one of the innovative AI platforms to overcome such roadblocks. This simple and unified AI platform helps developers and researchers learn, develop, and deploy AI.
With the mass adoption of AI technology in the FinTech Industry, participants, users, and policymakers have to face other risks too, like:
● Representation and relevance of data
● Data privacy and confidentiality
● Risk of bias and discrimination
● Auditability of AI algorithms and models
● Risk of tacit collusions & competition dilution
The key takeaways — AI in FinTech
The use of AI in the finance and FinTech sector is expected to increasingly drive competitive advantages for financial institutions through two key channels:
(1) By increasing the profitability of the businesses by increasing their efficiency. They are driving high profitability through cost reduction, productivity improvement, improved decision-making processes, automated execution, gains from better risk management and regulatory compliance, back-office process optimization, and other optimizations:
(2) By enhancing the quality of financial services and products offered to consumers. They are reducing the gap between product & market by offering new personalized products & services.
