Generative AI as Strategy in the Financial Industry

ChatGPT is, no doubt, the latest massive internet phenomenon: 100 million users in two months, with an estimated 13 million users every day in January 2023, according to UBS and Similarweb. 

This is an unprecedented record in the digital age: TikTok,  the predecessor phenomenon, took nine months to reach 100 million users. Instagram took almost two and a half years and Facebook didn't get there until it was 4 and a half years old.

But what really matters is that ChatGPT (Generative Pre-trained Transformer) has popularized and made famous one of the most disruptive and versatile technologies of the digital age: Generative artificial intelligence (AI).

While Generative AI has the potential to revolutionize various industries, it also has the potential to reshape the financial industry in significant ways, unlocking a range of benefits and new risks for financial institutions. 

What is Generative AI?

“Generative” means "capable of creating or innovative producing." In the context of AI, it refers to the ability of an algorithm or model to create new content or data from existing data, imitating human creativity by generating realistic images, text, music or any other form of content. 

Real Potential Benefits for the Financial Industry.

Generative AI holds significant promise for the financial industry, with real applications, like these seven examples:

1. Regulatory Compliance and Reporting: Can identify potential compliance issues and generate reports more efficiently, helping financial institutions reduce the risk of penalties or sanctions.

2. Credit Scoring and Risk Assessment: Can be used to improve credit scoring and risk assessment processes by analyzing a wide range of data points, including traditional credit history and alternative data sources. 

3. Fraud Detection and Prevention: By training AI models on historical financial data, the system can identify unusual activities or transactions, helping financial institutions take preventive measures more effectively.

4. Customer Service and Support: Can be integrated into customer service and support systems to provide personalized assistance and improve overall customer experience, while freeing up human agents to focus on more complex tasks.

5. Trading: Can be employed to develop advanced trading algorithms that predict market movements and optimize trading strategies, identifying patterns and generating insights to inform investment decisions, leading to more efficient and potentially profitable trading outcomes.

6. Financial Forecasting and Analysis: AI-driven models can identify patterns and correlations that may not be apparent to human analysts, leading to more accurate predictions and improved decision-making for financial institutions.

7. Personalized Financial Products and Services: By analyzing customer data, preferences, and financial behaviors, AI-driven systems can generate customized recommendations for investment strategies, savings plans, or insurance policies, leading to improved customer satisfaction and loyalty.

Notable Examples and Use Cases.

Many financial institutions have begun to leverage Generative AI to be more competitive:

• Goldman Sachs uses Generative AI algorithms for algorithmic trading and investment strategies.

• American Express uses Generative AI models for fraud detection and prevention.

• JPMorgan Chase has implemented a system called COIN (Contract Intelligence) to automate the review and interpretation of commercial loan agreements, reducing the time and effort required for manual contract analysis, leading to significant cost savings and reduced risk of human errors.

• HSBC, Bank of America and Royal Bank of Scotland (RBS) introduced Generative AI-driven virtual assistants and chatbots to provide personalized financial guidance and support to their customers.

Risks and Challenges. 

Despite its potential benefits, Generative AI also presents risks and challenges for the financial sector, including data privacy and security, bias in training data, regulatory compliance, intellectual property and copyright issues.

Overall, Generative AI can significantly impact the labor market in the financial industry, creating new jobs and redefining others:

1. Potential Job Displacement: Regulatory compliance and risk management, administrative/repetitive tasks, trading and investment strategies.

2. Potential Creation of New Jobs: AI-Fraud detection and cybersecurity experts, financial advisers and wealth managers who can leverage AI-generated recommendations, financial analysis will increase demand for professionals who can effectively utilize AI-generated insights, new job roles focused on addressing ethics and responsible Generative AI deployment.

3. In Any case, Skill Shifts and Retraining: Increased emphasis on skills like data analysis, AI literacy, and understanding strategic decision-making based on Generative AI.

Its speed and depth will depend on several factors, such as the AI adoption rate of companies/regions, the adaptability of workers, and the policies put in place by governments and organizations to manage the transition. 

This trend goes beyond technology, it is a Global Company Strategy.

For a financial institution to be successful with Generative AI, several key factors need to be considered, but we will focus on the three main strategic decisions to achieve a real impact on the company's business.

• First is talent profile and its location within the organization. As we commented, Generative means "capable of creating or innovative producing." This implies that it is critical to assemble a 100% specialized team, within a separate area of the operation, such as innovation, specialized in creating new digital, competitive capabilities for the company. One of the most common mistakes is to mix creation profiles within operations structures.

• Second is to choose/prioritize, together with the business areas, the use case to start developing Generative AI capabilities, working together and focused on customer-centricity to generate more business.

• And third: once the first successful use case has been achieved, the area specialized in creating new digital business capabilities must deliver it to the area of operation, so that it can fulfill its mission to scale this first use case and maintain it. At this moment, the specialized team will begin with a new business case. This is the virtuous circle of creation/innovation.

The financial business of the 21st century is a business of digital data exchange and information-based decisions. This is the reason why business-oriented Generative AI tends to be a highly strategic instrument, not an operational tool. 

The human factor in leadership, skills, relationships, critical thinking and creativity cannot be replaced. It is precisely for this reason, as in any strategic project, that without the support of top management and, above all, without the right talent and the right leadership, it will become an expense, instead of an investment.