AI has found meaningful use cases across industries, and the finance sector isn’t an exception. From banks to fintech startups, every organization in the finance industry is embracing AI to automate and streamline mundane tasks, increasing operational efficiency and cost.
This article lists 12 notable AI-powered innovations that are changing the face of the finance sector.
Automated Fraud Detection & Prevention
A PYMNTS report revealed that 62% of financial institutions with over $5 billion in assets have witnessed growing financial crimes. While traditional fraud detection systems helped to some extent, their static, rule-based algorithms weren’t entirely successful in flagging suspicious transactions.
Financial organizations are now turning to AI-powered solutions, like FraudGCN, to analyze patterns across massive financial data and corporate relationships to identify and predict fraudulent activities. FraudGCN has outperformed current fraud detection and prevention approaches by 3.15% to 3.86%.
Algorithmic Trading Systems
Today’s complex modern trading systems demand a robust solution that can execute larger order volumes with greater precision and minimal market impact. Algorithmic trading systems driven by advanced AI have become a promising solution to this challenge.
AI algorithms can optimize trade execution strategies, analyze market trends, and predict price movements with precision, making them the perfect cost-efficient option to aid profitable trades. The algorithmic trading market will be worth $53.91 billion by 2032.
Quantitative Analysis for Investment Insights
AI makes it easier to process massive, unstructured, and complex financial data to gain relevant insights in less time. It is transforming how investment research is conducted. Many American companies have started using BlackRock’s Aladdin AI to conduct quantitative analysis.
The tool’s advanced quantitative models and data analysis capabilities allow investment professionals to perform stress tests, complex portfolio modeling, ‘what if’ scenarios, etc., to aid informed investment decisions.
Process Automation for Efficiency
Numerous repetitive back-office processes in the financial sector can be automated to save time and operational costs. It is why organizations are adopting advanced AI solutions like Robotic Process Automation (RPA).
According to a McKinsey report, approximately 60% of the participating companies have already implemented AI-driven RPA solutions to handle several tasks. Since RPA bots can automate many middle-office, customer-facing, and back-office processes, companies can save money by hiring fewer employees for work.
Personalized Banking through Chatbots and Robo-Advisors
Chatbots and robo-advisors have opened a sea of possibilities to automate repetitive tasks to boost operational efficiency, customer experience, and cost-saving opportunities. Some use conversational AI chatbots to manage routine inquiries, while others use robo-advisors to offer personalized investment recommendations, expense management, etc.
Big players like JPMorgan are exploring over 300 AI use cases to identify better automation opportunities. They are also building IndexGPT, a ChatGPT-like AI tool to pick the best investments for customers. These efforts help organizations save money while providing greater customer experiences.
Sentiment Analysis from News and Social Media
AI-powered sentiment analysis is a powerful tool to interpret the emotional tone of online posts, news articles, and other textual data to better and more accurately predict market trends. It can also help identify potential risks by monitoring news feeds so professionals can adjust their investment strategies to prevent adverse market reactions. Many American financial firms are using sentiment analysis and other AI tools to customize customer experience like never before.
Advanced Credit Scoring
AI has transformed how financial institutions evaluate the creditworthiness of potential customers before making lending decisions. Since advanced AI and machine learning algorithms can analyze historical and real-time data from numerous sources, identifying patterns to anticipate financial risks becomes easier. Financial institutions offering AI-enabled lending have reported 53% fewer defaults at the same approval rates.
Predictive Analysis for Customer Retention
Predictive analytics utilizes machine learning techniques, statistical algorithms, and historical data to make precise predictions about future events or outcomes.
While predictive analytics has several use cases, banking institutions are increasingly using it for customer retention. They leverage churn analytics to analyze customer behavior and predict the likelihood of customers discontinuing their relationship with a bank. This information can help them create targeted strategies to retain customers.
Interpreting Commercial Loan Agreements
Major banking institutions use a significant portion of their workforce, including lawyers and loan officers, to interpret complex and lengthy commercial loan agreements. While some financial institutions still use the traditional approach of interpreting commercial loan agreements, big banks like JP Morgan Chase & Co. are leveraging AI to automate this task.
This American giant launched a machine learning-powered COIN (Contract Intelligence) program to review legal loan agreements within minutes. Since implementation, the bank has saved 360,000 hours a year and decreased loan-servicing mistakes.
Personalized Marketing and Recommendations
AI has become sophisticated enough to analyze gigantic volumes of user data, including financial habits, user behaviors, preferences, etc., to provide customized recommendations through interactive chatbots and other gen AI tools. It is also assisting personalized marketing efforts by scaling the operations at task.
Klarna, a Swedish Fintech company, has reported using generative AI to handle tasks that would have otherwise required 700 full-time employees. The company saves $10 million every year by using AI in its marketing department alone. Klarna now uses AI applications like Midjourney, Firefly, and DALL-E for image production, resulting in massive savings.
Behavioral Biometrics
The rising threat of cyber fraud has made financial institutions turn to advanced AI solutions to protect their customers. Behavioral biometrics is a novel approach to user authentication that uses big data and machine learning to distinguish between fraudsters and legitimate customers.
This tech analyzes unique user behaviors like mouse movements, typing patterns, navigation habits, etc., to verify user identities and prevent fraud. Banking institutions can use it to prevent online fraud and support other risk management processes.
RegTech for Regulatory Compliance
All financial institutions have to comply with various regulations and ensure their policies are constantly aligned with newer amendments. It is a tedious task that requires significant manpower and investments. However, the introduction of RegTech aims to end these challenges. This tech uses AI to automate compliance by scanning documents, analyzing transactions, identifying non-compliance risks, etc. It significantly reduces the need and cost of manual audits, helping organizations focus more on strategic initiatives.
