Introduction
The primary basis for using these set of technologies, simply put is the ability of AI to swiftly analyse large amounts of data to assess trends, foresee future performance, & permit investors to estimate growth and risk. Another benefit of AI in this sector is its massive scalability.
This blog explores the emerging trends and current use cases where AI is already making a significant impact.
Table of Contents
Use Cases & Trends of Artificial Intelligence in the BFSI Sector
As per the source Statista, “Data analytics maintained its position as the leading AI application among financial services firms in 2024. A 2024 industry survey indicated that 57 percent of companies leveraged AI for data analytics, showing modest growth from the previous year.”
The potential versatility and use case of AI technology in BFSI is endless. However, the increase & sequential spending on AI is corroborated with certain trends within this sector.
1. Cybersecurity and Fraud Prevention
Algorithms in AI, can effectively be used to pinpoint anomalies and suspicious outlines in financial transactions, helping to prevent fraudulent activities & losses. Cyber threats actively target sensitive financial data, which can be easily sourced by cybercriminals and unknown threats.
A classic use case in BFSI sector is Denmark’s largest bank, Danske Bank which effectively leveraged AI in fraud prevention. “Implementing a fraud detection algorithm powered by deep learning, the bank experienced a 50% increase in fraud detection capabilities and a 60% reduction in false positives.”
Source: TechMagic
One of the main benefits, also of using AI in this field, is its ability to monitor real time effects of cyberattacks in a swift and precise manner before a security event even occurs.
Other use prominent use cases in this trend, are:
Malware detection – Analyse suspicious patterns within E-mail(phishing), Files, User behaviour and network data, etc.
Vulnerability Management in Banking software – Assess weak points and gaps in networks and systems so Banks can focus on important security tasks.
A practical example is how JP Morgan Chase reduce a 20% Payment validation rejection rates in fraud management, using AI which lead to significant cost savings.
Source: Ernst & Young
2. Customer Service & Experience
- Use of AI can significantly enhance user experience, through tailormade experiences, and banking product recommendations to provide personalized financial advice which may lead to greater satisfaction and loyalty.
- Furthermore, banking site chatbots are increasingly being used mainly to not only streamline interactions, but to improve customer engagement. Another prime benefit of using bots is its 24/7 availability to aid with tasks. Offering omni channel support, process automation, can largely reduce costs, and the possibility of human error.
- Automated KYC is another use case of AI to verify information from various documents, significantly reducing processing times and improving accuracy. An example is DBS Bank of Singapore which reduced credit card application processing times from 5 to 1 days using AI which improved customer satisfaction.
For Example – Bank of America’s virtual assistant, Erica, is a prime example of AI in this BFSI sector. This virtual assistant provides personalized financial advice, responds to queries, and further alert customers about potential issues or opportunities.
Source: Cloud 4C
3. Risk Management & Compliance
Risk management and compliance is a key function for players in the broad BFSI sector. For Banks, AI serves many purposes.
In Banking, firms are increasingly integrating AI into proprietary systems automated document review, and automated text- based reporting. Since banks rely extensively on monitoring risk, AI can be used for various modelling purposes such as credit risk, operational risk and market risk assessment.
For example – “Standard Chartered, for instance, is using AI to improve their transaction monitoring system. This helps them spot suspicious transactions quicker, making their anti-money laundering (AML) efforts more effective.”
Source: Data Sniper
In the wide field of Insurance, risk and compliance is a very important division/trend which can be automated. Since the BFSI sector uses traditional non-AI monitoring tools, cases of false positives reach up to a huge proportion of 90%. – This is a very large proportion.
Source: Lucinity
4. Insurance Sector
This sector is on the verge of a significant paradigm shift, where AI is primarily focused on:
Personalized Dynamic Pricing: Apart from personalizing insurance policies, AI helps to assess risk profiles of customers in an automated manner, which aids in attracting a wider customer set and allows improvement in risk.
In some cases, premiums are calculated in real time as per customer’s ongoing habits, health data, etc.
Ex: Metromiles’ Pay-Per-Mile Car Insurance utilizes AI to assess driving behavior, adjusting premiums accordingly.
Source: Deloitte
Automated Underwriting: Right from document summaries and claims servicing, AI can easily supplement human professionals. Although there is a risk of bias (being overweight toward certain potential policy holders) using data, automation can greatly reduce cost and time.
Legal Compliance: By permitting firms to be up to date on the changing regulatory frameworks, AI can improve decision-making, which will result in clear error reduction and cost savings.
5. Data Analytics
AI models can analyze historical data to predict trends, calculate claim probability, and improve pricing schemes.
As per Statista, “The financial sector’s spending on Artificial Intelligence (AI) is projected to experience substantial growth, with an estimated increase from 35 billion U.S. dollars in 2023 to 126.4 billion U.S. dollars in 2028.”
AI’s high growth in other usage fields is undermined by its versatility in the analytics sphere. As per Hewlett Packard, “AI can quickly analyze large volumes of data to identify trends and help forecast future performance, letting investors chart investment growth and evaluate potential risk.”
Conclusion
The ingenuity of AI in Global BFSI has innumerable use cases, many of which have been undiscovered and are in the exploratory phase. Since the BFSI sector offers services and products (Mortgage, Travel Insurance, Line of Credit, etc) which have many potential variations, the resultant usage and growth of AI is limitless.
