How AI is reshaping the FinTech landscape, from enhancing trading strategies and credit underwriting to improving risk management and fraud detection.
The Gist
- Insightful AI.FinTech firms are using AI to provide financial insights.
- Enhanced underwriting.AI tools are improving accuracy and inclusivity in underwriting.
- Proactive management. Explainable AI is changing the face of risk management.
The rapid advancement of technology is driving major transformations in various sectors, and finance is no exception. Fintech companies are leveraging artificial intelligence to analyze large amounts of financial data and provide insights, recommendations and forecasts to customers, enabling them to optimize their financial performance and goals. AI is also helping financial service providers protect themselves in terms of risk.
Below are four examples of how AI is being used to improve advice to customers while ensuring that the financial service providers optimize their own goals as well.
1. Using AI to Offer Relevant Advice to Traders
TradeZing is using AI to pull relevant information for traders to help them create a more refined investment strategy, said Jordan Edelson, co-founder and CEO. “AI can analyze historical market trends for stocks or assets and compare it to previous performance to provide a recommendation on how to invest at a rapid pace. While this can be done for obvious events including news, economic decisions or earnings reports, AI can also bring in sentiment scores that stem from geopolitical issues or natural disasters which have the potential to impact markets.”
Investors who leverage AI tools for strategy will have a competitive advantage when it comes to trading, Edelson added. “There will be a widening gap between people with access to this information while others will be left behind. Learning how to interact and use AI will become one of the biggest learning curves for investors.”
Mastering the use of artificial intelligence in investment strategy is not just an advantage, but a necessity for those who seek to thrive in the increasingly complex landscape of financial trading.
Related Article: How AI Is Being Used for Consumer Education in Banking
2. AI Ushers in a New Era of Precise and Inclusive Underwriting
AI is proving to be especially valuable in credit decisioning, according to Jennifer Fuller, partner at PA Consulting.
“Smarter underwriting practices using AI has not only led to more accurate assessments but also opened the door to some borrowers who were in the past declined,” Fuller explained.
Fuller pointed to California-based Zest AI, which conducts underwriting specialized for consumers with little or no risk. The Zest AI platform searches out thousands of data points — more than the typical underwriting solution — to help assess a borrower’s risk.
This wouldn’t have been possible pre-AI, Fuller said. “This has added a new avenue of borrowing for some, but equally impressive they have also stated that companies using their platform have seen a reduction in risk and losses of almost 25% versus traditional underwriting practice. A win-win all round it seems.”
The advent of AI in underwriting is not just enabling more precise risk assessment but is also fostering financial inclusivity by extending credit opportunities to previously underserved individuals, all while significantly reducing risk and losses for companies.
Learning Opportunities
Related Article: 4 Ways Financial Services Provide Next-Level Customer Experience
3. Harnessing Explainable AI for Proactive Risk Management
BondIT uses its proprietary explainable AI (XAI) and machine learning (ML) to equip portfolio managers and advisers with the ability to anticipate changes in the credit risk profiles and rating transition probabilities of corporate and financial issuers, to better manage risk and capitalize on investment opportunities ahead of the market, said David Curtis, head of global client business.
BondIT’s credit analytics platform, Scorable, analyzes more than 250 unique variables totaling 350 gigabyters daily and translates raw data from a vast array of sources, including financial statements, fundamentals and capital market data into actionable insights for investors, Curtis explained.
Curtis added that the Scorable platform uses an explainable AI approach, which supports transparency and allows users to understand the drivers behind the risk assessments and gives them the ability to see the individual impact of variables for any issuer at any given point of their history.
4. Leveraging AI for Enhanced Fraud Detection
Financial services providers and their customers both want hackers deterred from compromising accounts, an area where AI is playing an increasingly important role, according to Isaac Patka, co-founder of Shield3.
Decentralized finance applications provide unprecedented transparency and data availability to train and adapt models for common mistakes by developers, attack patterns by bad actors and penetration testing by benevolent hackers, according to Patka. “For example, one can now visit a blockchain explorer, copy the code of a smart contract from a popular DeFi app, and paste it into ChatGPT, asking it to find potential ways the code can be exploited.”
Similarly, a person can ingest all of the data about all smart contracts and transactions in existence and identify patterns and transactions that lead to a major hack, Patka added. “When someone is about to attack a protocol there are often a series of transactions where they create a new anonymous wallet using a private transaction service, like Tornado Cash, then prepare their wallet to exploit a protocol. Protocols can defend themselves by detecting these patterns and pausing the protocol before the exploit can take place, then implement fixes before unpausing.”
While this data is widely available, the vast majority of users have difficulty understanding it, Patka explained. “AI tools allow us to take the insights from threat analysis and detection tools and present them in language which is personalized and comprehensible to everyone, regardless of their level of technical sophistication. We can take highly technical audit reports and data streams and have large language models summarize the threat in any language for any audience.”
These tools allow us to both detect threats faster and more efficiently than ever before,and democratize access to the insights to make security and risk mitigation widely available, Patka added.
Final Thoughts on AI in Fintech
“ChatGPT and similar generative AI applications are garnering a lot of hype,” points out Justin Norwood,nCino vice president and data and AI lead. “However, for banks and credit unions, the real value of AI and machine learning is currently found in more pragmatic applications like automating processes, improving customer experiences and optimizing pricing and profitability. At its core, AI should augment what the FI is already striving to do: power better, faster and more personalized user experiences. Financial institutions can realize the greatest benefits from AI’s powerful capabilities when they are fully aligned with their strategic priorities.”
In sum, artificial intelligence is increasingly becoming an indispensable tool in the realm of financial technology, driving advancements that are improving both customer experiences and institutional efficiency.
