AI influential on Banking

THE POWER OF GENERATIVE AI IN BANKING AND FINANCE

INTRODUCTION

Probably, we are aware that AI is projected to be a key driver in banking transformation by 2024. According to industry experts, AI is poised to revolutionize customer propositions, streamline operations, and unlock new possibilities for personalized and efficient banking services. Banks' strategic choices will be tested as they contend with multiple fundamental challenges to their business models. They must demonstrate conviction and agility to thrive. The banking industry has long been familiar with technological upheavals, and generative AI stands as the most recent influential development. This advanced machine learning technology, adept at sifting through vast data volumes, can generate distinct insights and content. Leveraging?Generative AI in banking?to collect and interpret financial data on a large scale empowers bank managers to make knowledgeable choices, offer tailored services, evaluate risks, and undertake additional tasks. Let’ dive into more details and use cases.

What Does Generative AI mean to Banking and Finance Industry?

Generative AI is bolstered by?machine learning models?and is dramatically transforming banking and financial services. The landscape of AI and automation for Banking is vast, offering a unique approach to automating tasks that were previously laborious. But what are the specific applications? We’ll highlight the top seven use cases. These include reshaping?customer service through AI-based Chatbots, employing AI for enhanced fraud detection, using machine learning to predict financial trends, and customizing banking services for individual needs.

7 KEY USE CASES OF GENERATIVE AI IN BANKING AND FINANCE

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 among numerous integrations occurring throughout the fintech sector.

While we’re still in the early stages of the?Generative 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 the banking industry.

1. Detect and Prevent Fraud

One major use case for AI in banking is preventing fraud. According to?Cybercrime 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 utilizing?Generative AI in Insurance?for 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. Banks?can 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 use?Generative 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 AI?can 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 credit worthiness, 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 relationships?in 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.

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5. Boost the Customer Experience & Customer Satisfaction

As a bank, you don’t just want to gain new customers. You also want to retain existing ones. And to do that, you must always improve customer service and invest in creating a good customer experience. When powered with?natural language processing?(NLP), Generative?AI chatbots?can provide human-like customer support 24/7. It can answer customer inquiries, provide updates on balances, initiate transfers, and update profile information. But it can also respond to more complex inquiries. For example, a customer may need help understanding how much of a mortgage they can afford. When AI models are provided with the relevant details such as interest rate, down payment amount, and credit score, Generative AI can quickly provide an accurate home purchasing budget. In this way, it can conversationalize complex math questions. Generative Artificial Intelligence can also educate on other financial tasks and literacy topics more generally by answering questions about credit scores and loan practices—all in a natural and human-like tone.

In addition,?Generative AI can personalize the customer experience. How? By analyzing customer data and then making personalized product recommendations. For example, it can recommend a credit card based on a customer’s spending habits, financial goals, and lifestyle. This makes for a convenient way for customers to pick the right card. Generative AI can also help you cross-sell and upsell products this way. According to a?study by Forrester, 72% of customers think products are more valuable when they are tailored to their personal needs. Conversational AI, a subset of Artificial Intelligence, can enhance user accessibility by simplifying the provision of multilingual support through virtual assistants and aiding those with disabilities through text and voice navigation options. Using?conversational AI in the banking?sector has become increasingly prevalent in recent years. Major financial institutions such as?Bank of America?and Wells Fargo have integrated this technology as the backbone of their?AI virtual assistants. These AI-driven platforms not only improve customer experience by providing instant responses and personalized interactions but also streamline numerous banking processes.

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Leveraging Machine Learning in Banking Industry in India

The use of Generative AI and?machine learning in banking?is not limited to the US or Canada. Financial institutions and banks in?India?are also utilizing machine learning for applications such as voice assistants and fraud detection. For example,?SBI Card, a payment service provider in India, leverages Generative AI and machine learning to enhance their customer experience.

6. Generate Financial Advice for Customers Based on Proprietary Data

Some financial institutions like mortgage brokers or investment companies provide financial advice to their customers. This can be one of the best Generative AI use cases for financial service companies. Such financial advisors and businesses can combine human expertise with the power of AI to give consumers more comprehensive and customized financial plans. First, you must train the Generative AI on your customers’ financial goals, risk profiles, income levels, and spending habits. From there, you can use it to make personalized budgeting and saving recommendations. The same goes for investing. Generative AI can make suggestions based on customers’ financial goals, income, and time horizons. For financial planners, this can lead to smarter investment & wealth management and trading decisions.?For example, Generative AI can assist in optimizing portfolios, managing risk, and executing trades. It empowers asset managers to make data-driven that aligns with their client’s financial goals and risk tolerances.

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7. Summarize Large Documents

Banks are notorious for dealing with massive amounts of paperwork. But manually sorting through, analyzing, and signing off on various financial documents and applications can take a lot of time and money. To cut operational costs, banks can have Generative AI comb through large volumes of documents to identify important data or summarize them for review. For example, Generative Artificial Intelligence can be used to summarize customer communication histories or meeting transcripts. This can save time when dealing with customer concerns or collaborating on team projects.

Challenges and Limitations of Using Generative AI in Banks & Financial Services

Of course, working with Generative AI in the banking sector has its challenges and limitations. It’s not a magic bullet that can do everything. It’s just a tool.

For example, Generative AI should be used cautiously when dealing with sensitive customer data. It also shouldn’t be relied upon to stay compliant with different government regulations, such as the General Data Protection Regulation (GDPR) or the General Data Protection Regulation (CCPA).Another limitation of Generative AI is that it can produce incorrect results if it’s fed with poor or incomplete data. So you always need to ensure your data is accurate and up to date. Otherwise, it could lead to poor financial decision-making.

As a rule of thumb, you should never let Generative AI have the final say in loan approvals and other important decisions that affect customers. Instead, have it do all the heavy lifting and then let financial professionals make the ultimate decisions. All that said, Generative AI can still be a powerful banking tool if you know how to use it properly.

Getting Started with Generative AI in the Financial Services Industry

At the end of the day, banks must learn to embrace Generative AI to survive. Not adopting the tool is letting your competitors get the upper hand.

With Generative AI still in its infancy, now is the time to learn how to implement it in your business. Your business can then evolve with it. Slowing global economy, coupled with a divergent economic landscape, will challenge the banking industry in 2024.?Banks’ ability to generate income and manage costs will be tested in new ways. Multiple disruptive forces are reshaping the foundational architecture of the banking and capital markets industry.?Higher interest rates, reduced money supply, more assertive regulations, climate change, and geopolitical tensions are key drivers behind this transformation.

The exponential pace of new technologies, and the confluence of multiple trends, are influencing how banks operate and serve customer needs.?The impact of generative AI, industry convergence, embedded finance, open data, digitization of money, decarbonization, digital identity, and fraud will grow in 2024. Banks, in general, are on sound footing, but revenue models will be tested.?Organic growth will be modest, forcing institutions to pursue new sources of value in a capital-scarce environment. Investment banking and sales and trading businesses will need to adapt to new competitive dynamics.?Forces like the growth of private capital will challenge this sector to offer more value to both corporate and buy-side clients. Early 2023 shocks to global banking have galvanized the industry to reassess their strategies.?While bank leaders focus on proposed regulatory changes to capital, liquidity, and risk management for US banks, there is much to be done to evolve business models.

CONCLUSION

A slowing global economy coupled with a divergent economic landscape will challenge the banking industry in new ways in 2024. Although recent efforts to combat inflation are showing signs of success in many countries, the risks brought to light by supply chain disruptions, rewiring of trade relationships, and ongoing geopolitical tensions will complicate economic growth worldwide. Extreme weather-related events, such as floods, heat waves, and hurricanes, may also cause severe economic disruption.

With this backdrop, the International Monetary Fund (IMF) expects the world economy to grow at no more than 3.0% in 2024.?Advanced economies—i.e., the United States, the Euro area, Japan, the United Kingdom, and Canada—are forecast to experience tepid growth at 1.4% in 2024. But many emerging economies should see higher growth on the back of strong consumer demand, younger demographics, and improving trade balances. In particular, India is expected to have one of the strongest growth rates: 6.3% in 2024. On the other hand, China is facing a potential economic slowdown with weak consumer demand and distressed property markets. The weakness in Chinese exports and imports will not only impact its trading partners, but may well challenge supply chain dynamics and further weaken global recovery. Recent efforts to revive consumer and corporate confidence in China could influence economic growth in other countries, particularly in Asia. Banks globally will face a unique mix of challenges in 2024. Each of these hurdles will impact banks’ ability to generate income and manage costs (both interest costs and operational expenses).