Generative AI: The Missing Piece in Financial Services Industry?

Just blink…out with the old ( ChatGPT …yup, almost already) and in with the (not so) new - Generative AI. So what is this Generative AI, really?

First, think of it as a combination of Angel and devil's advocate, who is a positive scrutinizer for good reasons. Did you get the picture of having them on each shoulder to steer you in the right direction? In summary, Generative AI is an advanced AI that involves creating intelligent machines to generate new data, images, text, or other types of content by analyzing large amounts of data to learn patterns and generate new content that resembles the training data. One of the most popular techniques used in generative AI is generative adversarial networks (GANs), which consist of two deep neural networks, a generator (Angel) and a discriminator (Devil's Advocate). While the generator creates content, the discriminator distinguishes between the real and generated content. After much debate (iterative process) between the angle and devil's advocate, the generator improves its ability to create realistic content, while the discriminator becomes more accurate at identifying fake content. See… we need a devil's advocate for a healthy debate with aligned goals to create value in the firm, right? :)

 Now that we know we will be able to create realistic scenarios using Generative AI without letting the boat leaves the shore, we could leverage it in the rapidly evolving financial services industry to address the top three use cases to revolutionize:

1. Digital Transformation
2. Fraud Detection
3. Risk Management

1.   Digital Transformation: the favorite one of mine…

It is the way to embrace digital technologies to drive innovation, efficiency, and growth to stay ahead of the competition. The financial services industry is ripe for digital transformation, with businesses seeking to improve efficiency, reduce costs, and provide better customer service. By embracing generative AI, businesses can create more value for their customers and stakeholders, stay ahead of the competition, and achieve long-term success. As the technology continues to evolve and mature, we can expect to see even more exciting possibilities for generative AI in digital transformation. If we slice and dice, we can create digital transformation in two phases, focusing on efficiency and growth built on innovation.

Phase1:Efficiency Focused -> Tackle Back office processes to optimize existing Value Streams

Pahse 2: Growth Focused -> Front Office Experiance to unlock New Value Streams

To create efficiency for the back office, the financial services industry has four major obstacles:

Four obstacles to change for Backoffice automation:

1.   Always focused on product release with little emphasis on process and Customer experience

2.   Complicated architectures due to Mergers and acquisitions, product launches, and regulatory changes

3.   Lack of skills/capabilities to introduce more automated processes. 

4.   IT may have different agendas and need more understanding of business priorities.

Let's look at some of the ways how generative AI can address the obstacles and augment digital transformation:

1. Improving Efficiency and Accuracy: It can improve efficiency and accuracy by automating processes that are prone to errors or inconsistencies. For example, consider how much accuracy is required to generate financial reports or forecasts to reduce the risk of errors or omissions to make better decisions and stay compliant with regulatory requirements.
2. Personalizing Customer Experiences: The best way to create customer satisfaction is by having the ability to provide personalized content to foster a better experience. Generative AI can help businesses personalize customer experiences by creating customized content, recommendations, and product suggestions. By analyzing customer data, generative AI can generate personalized marketing messages, product descriptions, and other content that resonates with individual customers to improve customer engagement and loyalty – an essential benefit of digital transformation. In addition to these benefits, generative AI can improve customer service by generating personalized financial advice based on individual preferences, goals, and risk tolerance to enhance customer satisfaction and loyalty - critical factors in the highly competitive financial services industry.
3. Enabling Better Decision-Making: Since all the stakeholders have agendas and lack the necessary understanding of business priorities to make better decisions, utilizing generative AI to analyze large amounts of data and generate insights and predictions so that businesses make more informed decisions about their products, services, and operations which can lead to better performance, increased competitiveness, and faster growth.
4. Lack of skills and capabilities: Not only can it address the lack of skills, but it also can enhance Creativity and Innovation by generating new and unique ideas that humans may not have thought of based on the data mining, which humans can use to decide on what new products, designs, or marketing campaigns can be created to stand out from the competition. It could be a huge differentiator in the marketplace and drive growth if used correctly.
5. Automating Workflows: With generative AI, businesses can streamline repetitive and time-consuming tasks, such as data entry, image or video processing, and content creation, to optimize the existing value stream. This can help businesses free up their employees' time to focus on more strategic and creative tasks, improving productivity and efficiency.

Then the second use case is Fraud Detection:

Financial institutions are increasingly vulnerable to fraud, and generative AI can help detect fraudulent activity in real-time and proactively - One of the most significant benefits. By generating synthetic data to train machine learning models, generative AI can help financial institutions identify patterns and anomalies that may indicate fraudulent activity, which can prevent financial losses and protect customers' financial information.

The third major use case is credit risk assessment:

Generative AI can provide more accurate and personalized credit assessments by analyzing factors such as credit history, income, and employment status to assess the creditworthiness of individuals and businesses. This can help financial institutions make more informed lending decisions and reduce the risk of defaults. 

Well... Who got on to the boat already? – Capital One & JMPC

 Both have been at the forefront of digital transformation in the industry. They leveraged generative AI to develop innovative solutions that improve their operations and services. For example, both tacked Fraud Detection using Generative AI and developed an AI-powered fraud detection system that uses machine learning algorithms to analyze transaction data and identify suspicious activity. The system has been highly successful, reducing false positives by 40% & 75% and improving fraud detection rates by 50% & 8%. The systems have also reduced the time it takes to investigate and resolve fraud cases, improving customer satisfaction and reducing costs.

Apart from these three primary use cases, there are a couple more to think about Portfolio optimization and Algorithmic trading to create differentiation in the market.

In conclusion, the future of financial services is bright with the potential use of generative AI. By leveraging the power of this advanced technology, financial institutions can transform how they operate, analyze data, and make decisions. From digital transformation, fraud detection, and investment management to credit risk assessment and customer service, generative AI can revolutionize every aspect of the financial services industry. I can't wait to see how Generative AI contributes to advancing the Financial services industry as it evolves and matures.

The time will tell!

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