Generative AI in FinTech and Banking

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Harnessing the power of artificial Intelligence, AI-driven financial counsel and portfolio administration are transforming how individuals and institutions manage their wealth.

According-to-Gartner-2024-Tech-Trends-

Here is a closer look at use cases of Generative AI in banking:

Detect and Combat Fraud

Fraud prevention is a major AI banking application. Many banks have large, expensive, and lumbering fraud prevention departments. GenAI can streamline these functions and deliver more accurate, fast, actionable fraud prevention insights.

Using Generative AI fraud detection tools, banks can track transactions by location, device, and OS. It can then identify unusual behavior. From there, bank staff can assess suspicious behavior and decide if it warrants further investigation. Then banks don’t have to manually review transactions, which is a time-consuming and error-prone process.

Generative Artificial Intelligence can also mine synthetic data and update its detection algorithms to detect new fraud schemes. This proactive and predictive approach will also prepare banks better to identify fraud.

Banks can use Generative AI to require additional account verification. AI chatbots may ask users for security questions or multi-factor authentication.

Risk Management, Credit Score Improvement

The credit business is an inherently risky one, and it needs risk management tools to avoid financial disasters. Generative AI can help banks assess credit risk, cost-effectively and fast.

Generative AI trained on historical data can identify financial risks and provide early warning signs. This can enable banks to prevent risk and reduce customer losses.

Similarly, for credit scoring, banks can use machine learning algorithms and AI to assess loan applicants. These tools allow banks to analyze massive amounts of data from multiple sources.

This helps create a holistic financial picture of loan applicants rather than traditional credit score elements.

Forecast finances

Training AI on historical financial data can help banks generate synthetic data. GenAI tools can use this data to create pretty accurate forecasts for the financial trends that banks should be aware of. These tools can find data patterns and run hypothetical simulations. From there, banks can assess various outcomes and plan accordingly.

This way GenAI tools can use past data to help banks make better and more effective financial decisions for their future.

Personalize Marketing

Like all businesses, banks need targeted marketing to attract new customers. However, specialized marketing for expanded customer engagement and market analysis is expensive and time-consuming.

Also Read: How Generative AI has transformed FinTech

GenAI provides tools that can accelerate the process of customer and market analysis based on behavioral data. This allows companies to divide their leads into smaller groups and create buyer personas. This exercise will help banks personalize their marketing strategies, getting a better chance at great customer engagement.

It can also help banks to customize marketing campaigns for different groups based on market trends.

With the data collated, GenAI can also help to generate targeted marketing materials and track conversion and customer satisfaction. It supports A/B testing to see what works and what doesn’t.

Improve Customer Experience and Satisfaction

For all enterprises, including banks, acquiring new customers is more difficult than retaining existing ones. Banks must constantly improve customer service and invest in innovative tools and strategies to accomplish that goal.

There are several ways GenAI and AI bots help them reach there.

Customer-facing chatbots can provide 24/7 human-like customer support. They can handle customer inquiries, balance updates, transfers, and profile updates. They can even provide inputs on complex queries seamlessly.

With a Big database, they intuitively know the needs, preferences, and requirements of each customer. With GenAI, their conversation is more human-like than ever before. All these attributes contribute to a much higher quality of customer engagement.

GenAI chatbots are perhaps better than human callers for customers who need help with more sensitive planning, like a mortgage budget. Generative AI can quickly calculate a home buying budget using interest rate, down payment, and credit score.

Generative AI can also answer questions about credit scores and loan practices accurately and instantly. It can also be useful for customers to get around-the-clock insights into other financial asks and needs.

It can deliver customized customer experiences through data analysis and personalized product recommendations. For instance, it may suggest a credit card based on a customer’s spending, financial goals, and lifestyle. Customers can choose the right card easily.

With the exhaustive data about the market, GenAI tools can also assist banks in cross-selling and upselling products. They can identify the target customer group, present the range of products and also the reason why they may be a good fit- all without any human intervention.

Use Proprietary Data to Give Customers Financial Advice

Banks that also work as mortgage brokers and investment firms may benefit most from GenAI AI tools. Financial advisors and businesses can use AI and human expertise to create more comprehensive and customized financial plans.

Data can help train the GenAI app on the customers’ financial goals, risk profiles, income, and spending habits. The insights they achieve can then make personalized recommendations for investment and savings.

Data on their income and financial goals can enable GenAI tools to suggest savings plans. This support will enable buyers to make better investment, wealth, and trading decisions.

These tools can also help customers to optimize portfolios, manage risk, and execute trades. From the bank’s perspective, it enables asset managers to use data, delivering suggestions that match client financial goals and risk tolerances.

Summarize Documents

Banks often struggle with paperwork overload. Manually sorting, analyzing, and signing financial documents and applications is a cumbersome task that takes resources and time.

Generative AI can identify important data in a document fast, or summarize large documents for review to reduce bank operational costs.

These tools can also summarize customer communication histories or meeting transcripts. This can save time on customer service and team projects.

This can revolutionize the delivery of banking operations and services, creating novel and unique business avenues. They enable huge efficiencies for banking operations, sometimes even changing how end users interact with banks.

Conclusion:

According-to-McKinsey-report-on-the-economic-potential-Generative-AI,--Across-the-banking-

By streamlining processes, improving client interactions, and offering insightful data for decision-making, generative AI has the potential to completely transform the banking and finance industries. Important use cases highlighting this cutting-edge technology’s broad potential include risk assessment, personalized customer experiences, fraud detection, and more.

Although there are certain obstacles and restrictions, like issues with data quality, privacy and security, and numerical accuracy, the potential advantages of generative AI greatly exceed these worries.

Financial institutions can open up new avenues for growth, innovation, and customer-focused services by embracing generative AI and utilizing its transformative power. This will pave the way for a financial world that is smarter, more efficient, and customer-focused.

Check Out The New TalkFintech Podcast. For more such updates follow us on Google News TalkFintech News.

Source: https://www.gartner.com/en/digital-markets/insights/2024-tech-trends-in-finance

Source: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier

Swapnil Mishra
Swapnil Mishrahttps://talkfintech.com/
Swapnil Mishra is a global news correspondent at TalkCMO, with over six years of experience in the field. Specializing in marketing technologies, Swapnil has established herself as a trusted voice in the industry. Having collaborated with various media outlets, she has honed her skills in content strategy, executive leadership, business strategy, industry insights, best practices, and thought leadership. As a journalism graduate, Swapnil possesses a keen eye for editorial detail and a mastery of language, enabling her to deliver compelling and informative news stories. She has a keen eye for detail and a knack for breaking down complex technical concepts into easy-to-understand language.

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