Generative AI tools are transforming the banking industry. The online payment platform Stripe, for example, recently announced its integration of Generative AI technology into its products. This is just one example of numerous integrations of AI in fintech.
While we’re still in the early stages of theGenerative Artificial Intelligence revolution powered by machine learning models, there’s undeniable potential for vast changes in banking. Verticals within financial services predicted to undergo significant transformation include retail banking, SMB banking, commercial banking, wealth management, investment banking, and capital markets. Let’s explore the seven use cases of Generative AI in modern banking in the USA, Canada, and India.
1. Detect and Prevent Fraud
One major use case for AI in banking is preventing fraud. According toCybercrime Magazine, the global cost of cybercrime was $6 trillion in 2021, and it’s expected to reach $10.5 trillion by 2025. To protect their business, banks must take data security seriously.
Many banks have large fraud prevention departments. However, these can be costly to run and maintain, and in some cases, they aren’t very effective.
Like utilizingGenerative AI in Insurancefor fraud detection, banks can use it to track transactions in terms of location, device, and operating system. It can then flag any anomalies or behavior that doesn’t fit expected patterns. From there, bank personnel can review the suspicious behavior and decide if it deserves further investigation. That way, banks don’t need to comb through transactions manually, which takes longer and is prone to human error.
In addition, Generative Artificial Intelligence can continually mine synthetic data and update its detection algorithms to keep up with the latest fraud schemes. This proactive approach helps banks anticipate fraudulent behavior before it happens.
Bankscan also use Generative AI to require users to provide additional verification when accessing their accounts. For example, an AI chatbot could ask users to answer a security question or perform a multi-factor authentication (MFA).
The point is there are many ways that banks can useGenerative AI to improve customer service, enhance efficiency, and protect themselves from fraud.
2. Manage Risk and Improve Credit Scoring
Banks can also use Generative Artificial Intelligence to manage credit risk assessment. Risk management is essential to avoiding financial disasters and keeping the business running smoothly. When trained on historical data,Generative AIcan detect and identify potential risks and financial risks and provide early warning signs so that banks have time to adapt and prevent (or at least mitigate) losses.
The same goes for credit scoring. Banks are in the business of evaluating borrowers applying for loans. Instead of relying on traditional credit score elements to determine creditworthiness, banks can have machine learning algorithms and AI to analyze vast amounts of data from multiple sources and create a more holistic financial picture of loan applicants.
3. Make Financial Forecasts
Another benefit of training AI on historical financial data is that it can help banks make financial forecasts and enable synthetic data generation.
Generative AI can identify patterns and relationshipsin the data and even run simulations based on hypothetical scenarios. From there, it can help banks evaluate a range of possible outcomes and plan accordingly.
In short, Generative Artificial Intelligence can look to the past to help banks make better financial decisions about the future and create synthetic data for robust analyses of risk exposure.
4. Personalize Marketing Efforts
Like all businesses, banks need to invest in targeted marketing to stand out from the competition and gain new customers. But this is easier said than done. It takes a lot of deep customer analysis and creative work, which can be costly and time-consuming.
However, Artificial Intelligence can help speed up your marketing efforts. How? By analyzing your customers’ preferences and online behavior. From there, it can split your leads into segments, for which you can create different buyer personas. That way, you can tailor your marketing campaigns to different groups based on market conditions and trends.
You can also use Generative AI to help you create targeted marketing materials and track conversion and customer satisfaction rates. Then perform A/B tests to see what’s working and what’s not. Over time, your marketing ROI will improve.
