Generative AI to Boost Global Banking Profits by $200-$340B Annually by 2025

Before jumping into how banking profits are affected, let us first understand the concept of generative AI and how it works.

Generative AI is the subset of artificial intelligence that employs machine learning algorithms to generate new information or content from existing data. 

Traditional AI generally analyzes or classifies preexisting data; however, generative AI can produce some completely new data, such as text or images, or even a decision model based on pattern knowledge. 

In the banking and finance industry, generative AI ingests trucking volumes of financial data, from customer interactions to market trends and transaction histories, to develop predictive models, automate processes, and provide suggestions to improve decision-making processes. 

These might be used for risk assessment, fraud detection, customer service, and investment strategies.

For example, generative AI allows banks to simulate very complex financial scenarios, predict market movement, or even produce highly customized customer communications to fit individual needs and preferences.

1. Productivity Gains

In banking, generative AI will help boost global profits of banking somewhere between $200 billion and $340 billion per annum by 2025. 

Mostly, this is realized through productivity enhancements, more streamlined workflows, and enhanced decision-making capabilities. 

AI tools, for example, process and enter data, process documents, and verify customers, reducing operation costs by huge margins and, in turn, enhancing productivity by up to 46 percent in certain areas such as India.

2. Fraud Detection and Risk Management

Fraud detection and fraud management are being swiftly transformed with Gen AI in the banking sector. The AI and machine learning algorithms have made it possible to interpret and analyze huge volumes of transactional data in real time and pick out suspicious patterns or anomalies. 

This is a huge step forward compared to the classical methods of fraud detection, which often use fixed sets of rules to thwart threats. 

AI models trained on the history of transactions can spot those out-of-the-ordinary and subtle patterns in fraudulent activities that often evade the human eye. This adds to the safeguards for both clients and banks, creating a safer environment for carrying out transactions.

3. Enhanced Customer Experience

Generative AI, the latest technology developed to increase revenue, enables customer experience enhancement largely via the automation of standard functions in customer service, financial advice, and the generation of real-time analytics. In fact, some banks consider AI their means of delivering personalized communications and so help their clients manage accounts and investments more efficiently. 

Each customer can receive personalized content, such as emails, financial advice, and recommendations, prepared by the AI model. 

Being able to provide personalized solutions on a large scale is what boosts customer satisfaction and loyalty. Banking profits are thus increasing.

4. Automating Routine Tasks and Reducing Costs

In the administration field, tasks such as risk and compliance testing, customer onboarding, and loan underwriting are being automated through generative AI. 

The respective banks have reduced human involvement in these tasks to increase efficiency, but at the same time, they have reduced expenses. In some instances, cost-saving reduction percentages may go as high as 60% in operational costs.

The use cases for generative AI in banking are diverse, offering a wide array of possibilities across the financial services sector.

1. Credit Risk Assessment

Generative AI is being used to enhance the credit risk assessment process. Traditionally, credit risk assessments relied on fixed criteria and human judgment, often resulting in biases or inaccuracies. However, AI-driven tools use vast amounts of data to generate predictive models that assess creditworthiness more accurately, considering a range of factors from transaction history to social media activity.

2. Anti-Money Laundering (AML) Compliance

AML compliance is a critical task for banks, and generative AI can automate much of the process. AI can analyze transaction data and generate reports that highlight potential cases of money laundering, reducing the need for manual checks. This not only saves time but also increases accuracy, helping banks avoid hefty fines and reputational damage.

3. Investment Optimization

Generative AI is also making waves in investment optimization. AI models can analyze market trends, historical data, and economic indicators to generate investment strategies that maximize returns while minimizing risk. This capability is particularly valuable for wealth management firms, hedge funds, and other financial institutions that rely on data-driven insights to guide their investment decisions.

1. Competitive Advantage

As the banking industry becomes more digitized, generative AI will provide banks with a significant competitive advantage. With AI handling routine tasks, banks can focus on high-value activities like personalized customer service and advanced financial analysis. This shift will allow banks to offer better services at lower costs, making them more attractive to both consumers and investors.

2. Meeting Customer Expectations

Today’s customers expect more than just basic banking services. They want personalized advice, seamless experiences, and faster transactions. Generative AI allows banks to meet these expectations by delivering custom-tailored financial solutions, responsive customer service, and intuitive user experiences. Banks that can leverage AI to offer these enhanced services will attract and retain more customers.

3. Future-Proofing the Industry

The future of banking lies in seamless human-AI collaboration. By 2025, generative AI will become an essential part of the banking sector, helping organizations not only automate routine tasks but also innovate and adapt to new challenges. By adopting AI now, banks are future-proofing their operations, ensuring they remain competitive in an increasingly AI-driven world.

Generative AI Industry in Financial Services

Generative AI within the financial service industry is exponentially growing, with major banks increasing capital outlay into AI technology to remain competitive.

A recent report indicated that 74% of financial firms have begun POCs for generative AI, and 11% are already deploying in full-scale operation. 

With AI adoption on the rise, those banks that welcome AI infrastructure as they would with cloud computing are reporting a 20-25% productivity increase across various departments, including risk, compliance, and service providers.

Generative AI Use Cases in Banking

1. Automated Underwriting and Loan Approval

One of the key applications of generative AI in the banking industry is in automated underwriting and loan approval. By using AI to analyze customer data, banks can streamline the loan approval process, reducing the time it takes to approve loans and offering more accurate credit assessments.

2. Enhanced Fraud Prevention

Fraud prevention is another area where generative AI is making a significant impact. AI models are capable of learning from historical transaction data, identifying potential fraud patterns, and generating alerts in real-time. By using AI, banks can detect fraudulent activity faster and more accurately, reducing financial losses and enhancing trust with customers.

3. Personalized Financial Advice

Generative AI in financial services also extends to providing personalized financial advice. AI models can generate insights based on a client’s financial behavior, preferences, and goals. This advice can range from investment strategies to savings recommendations, helping clients make more informed financial decisions.

As the generative AI industry in banking markets continues to grow, there is an increasing demand for professionals who understand the role of AI in financial services. To meet this demand, the GSDC Generative AI in Finance and Banking Certification is well-suited for anyone wishing to establish themselves professionally in this transformative technology. 

The GSDC Generative AI in Finance and Banking Certification is an international, exam-based certification that formally recognizes your ability to design AI-driven solutions in real banking environments.

This certification also proves your understanding of the employment of generative AI technologies for major banking functions such as fraud detection, credit scoring, compliance automation, and customer personalization.

The generative AI certification in banking is an alternative to an old-fashioned course, allowing you to get your hands on tried and tested expert templates and case studies, plus practical learning materials to quickly boost your employability in generative AI solutions. 

If you want to be certified in generative AI for banking, this professional certification pathway identifies you as being able to ethically and effectively implement AI within a variety of banking

The influence of generative AI on banking and finance is unquestionable. Even though AI is setting up this industry for a revolution in a manner that transcends disability-level automation, by 2025, it is projected to add between $200 billion $340 billion in annual profits. 

By creating greater productivity, better decision-making, fraud detection, and customer-level personalization, generative AI is all set to put the very foundation of a more efficient, customer-focused, and innovative banking on. 

Banks that will embrace generative AI now will successfully be fine-tuned for the future, thus ensuring that they will never lose their competitive edge or ever stand on the sidelines in this ever-evolving market. 

Since more and more developments are made on the AI technology front, the potential transformation that the banking industry can undergo is somewhat limitless, hence urging any financial institution that wants to survive this digital epoch to consider AI as its foremost consideration.