In 2020, Sandra Nudelman of JPMorgan Chase revealed that the bank had decreased fraudulent activities by $100 million a year using AI in an interview with VentureBeat. It resulted from the company’s yearly investment of $ 11 billion for technology upgradation. The bank leverages critical fraud detecting applications. In fact, they went a step ahead by sending the credit card transaction details to data centers that decide the legality of the transactions, thus bolstering users’ security.
JPMorgan Chase is not a one-shot incident. And certainly, AI’s invasion of the FinTech industry is not geography specific. Egypt’s EBank’s collaboration with IBM or Breaking Wave’s (a Deutsche Bank company) partnership with Relativity is enough to prove how global AI’s impact is.
The FinTech industry is a complex ecosystem where banks, financial service providers, startups, and others coalesce, and it thrives on innovation. AI and Machine Learning are disrupting the domain by empowering systems with predictive, intelligent models and reducing error counts of manual labor, among others. Such initiatives are driving the global AI in the FinTech market towards a valuation of $41.16 bn by 2030.
AI in FinTech: 5 Use Cases
AI is nothing new. As a word, it first popped in 1956 when scientist John McCarthy used it at the Dartmouth Conference. But it really made big when in 1997, IBM’s Deep Blue beat the world chess champion, Gary Kasparov. Today, it has branched out to several industries and ushered in significant changes.
1. Security & fraud detection:
In 2022, from January to April, there were 17 instances where the security of financial institutions came under threat. It is only the record of the known ones, but the amount lost is worth over a billion USD.
Stealing in banks or other financial institutions is not a new practice. What’s worrying is the galactic leap in the number of such incidents with digitization. Earlier, experts marked digitization for the BFSI sector as a must-do futuristic move. It still is. But the process has made the sector more vulnerable as it brought accounts closer to hackers’ reach. The need for security in finance has become crucial, and it is evident from the rising incidents of credit card scams, bogus insurance claims, and illegal wire transfers.
An effective AI system can monitor financial transactions in real-time, and it can be a perfect solution. With robust AI algorithms, you can easily find out abnormalities or suspicious patterns quickly, and then you can take immediate action to prevent further escalation in the gravity of the
fraudulent activities. AI in Fintech could also help financial institutions maintain a high level of accuracy in arresting such breaches.
2. Customer Support:
People want quick solutions, especially when their queries are linked to finance. Running round-the-clock customer service for 365 days is not just costly but also cumbersome for financial institutions. And on top of these, there are bad reviews, and drop in customer satisfaction and
Ally Bank was one of the frontrunners that recognized how AI could help with customer support. They introduced Ally Assist, a chatbot, in 2015 to direct customers toward their solutions. In 2016, Bank of America’s Erica became a huge hit. Chatbots are expected to handle 75-90% of healthcare and banking queries by this year’s end. In fact, chatbots are now an impressive tool for brand communication. AI-powered chatbots, virtual assistants, and other AI interfaces are quick in saving workload by efficiently handling the basic queries and concerns of the customers.
Yes, there are some complaints about chatbots or virtual assistants not providing human care. But with companies investing heavily in Machine Learning, particularly in Natural Language Processing (NLP), things are expected to change real fast. They are now learning to self-improve, manage complex conversations by using advanced sentiment analysis, and in the future, they will possibly learn to complete new tasks without training.
3. Asset management:
A report by Mckinsey quotes, “The application of advanced analytics to specific business problems has begun to deliver value for traditional asset managers — not by replacing humans but by enabling them to make better decisions quickly and consistently.” But there is a catch. Finance is proprietary. Joshua Pantony, in a Forbes article, recently revealed that most of the financial AI experts are embedded into organizations, and these companies do not have a cross-collaboration or open-source mentality.
In addition, Joshua adds, there is also a reliability issue. It is difficult to understand why to trust a specific insight and change your strategy accordingly. However, AI’s capability to identify insights from structured and unstructured data is unparalleled and less time-consuming. By using NLP, asset management companies can extract insights, generate summaries and simplify the decision-making process. Also, with no-code AI, investment firms can now automate manual processes.
Not only this, but they can also offer brand new services like wealth management tools. With AI and ML, users no longer have to deal with intermediaries for carrying out their day-to-day bank-related activities, and it could reduce operational costs.
4. Credit Scoring and Loan prediction:
Around one in four Americans rely on high-fee alternative financial systems and are underbanked, which makes getting a loan or credit card extremely difficult. It can lead to poverty and an economic crisis. This makes access to traditional financial systems all the more necessary.
But, traditional credit scores are now outdated as they fail to factor in employment history and financial behavior. On the other hand, AI’s ability to assess spending patterns provides more accuracy when it comes to reviewing risk.
For better implementation of AI in Fintech, it is crucial to have data diversity. Otherwise, there is a chance of getting biased insights. A recent article in MIT Technology Review revealed bias data tend to influence AI’s decision-making ability. The chances of getting a loan sanctioned in the US for minorities are less because the system lacks information about their credit history.
5. Predictive analytics and forecasting:
Forecasting is difficult in the finance sector as outside factors can always disrupt the existing setup. But it is also an unavoidable practice as it depends on fund distribution, timely and accurate decisions of borrowing or investing, and maintaining target balances.
Predictive analysis has completely revolutionized the way in which financial companies make decisions. It leverages AI and ML to help companies analyze a wide range of customer and market data. AI identifies patterns better than the human workforce. These patterns could be repetitions in payments like random sequences of letters and numbers. Moreover, AI can make assumptions, which along with its ability to test and learn autonomously, is an advantage the FinTech industry can leverage.
Predictive analytics can also be applied to FinTech’s marketing and branding campaigns for evaluating the effectiveness of a campaign on a customer.
Future of AI in FinTech
AI’s inclusion in the finance sector dates back to 1986 when APEX (Applied Expert Systems) introduced PlanPower. It was a commercially applied AI financial technology to help people with an annual income of over US$75,000 per year plan their investments better. Since then, a lot has
changed in the sector. It has radically transformed the way we collect data, analyze, protect and facilitate transactions, build customer-centric products, and streamline processes.
The future of AI in FinTech is going to pivot around four crucial aspects- more need for personalization, rapidly increasing digitization, regulations to ensure ethical sides, and security.
New regulations introduced by the European Union is a proof of how much the governments are concerned about the ethical practices of AI. The intent is to ensure people’s safety or fundamental rights, which can be threatened with AI-enabled behavior manipulation techniques.
Investments in data diversity are expected to skyrocket. Biases, due to a lack of diversity in training models, are impacting lives, and companies are trying to plug those holes by improving their data collection methods.
Demand for better personalization is driving companies to invest in AI models that can decipher voice and speech patterns and predict customers’ moods to offer better solutions. AI is evolving with the continuous insight influx, and it is getting better at reducing trading risks.
Any bank or FinTech organization that isn’t adopting AI will surely fall behind. If you are a financial service company, a FinTech startup, or a bank struggling to implement AI/ML, then you must read our checklist on AI/ML implementation.
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