Generative artificial intelligence is a type of technology that has come to increase productivity and performance in the daily performance of various sectors, becoming one of the trends of great impact for the financial sector.
According to the AI Risk Survey Report conducted by KPMG in September 2022, 85% of respondents expect an increase in the use of AI models and predictive analytics and half of respondents stated that they see a positive ROI from investments in AI technology. In this race, from OpenAI and Amazon to Google and Microsoft are devoting resources to advances in artificial intelligence.
What is generative artificial intelligence
Artificial intelligence is a term used to classify machines that mimic human intelligence, while generative AI is a type of system capable of generating text, images, and other content in response to natural language requests. Generative AI produces this new content, such as synthetic data, which is information generated from real data samples to train models. The algorithm is based on learning patterns and correlations, and once trained, can generate identical data.
Generative AI can be divided into five categories: content generation, information extraction, intelligent chatbots, language translation, and code generation. In this article, we’ll focus primarily on how generative AI contributes to the areas of fraud detection and prevention, data privacy, risk management, and personalization of the customer experience.
Applications of Generative AI in Financial Services
Fraud detection and prevention
This aspect is one of the priority concerns for banks, cooperatives and financial solutions companies. Exploring advanced technologies, such as generative AI, makes it possible to improve pattern detection capabilities and/or predictive models.
By training models on large data sets, it is possible to generate synthetic data that simulates the characteristics and behaviors of fraudulent activities. This synthetic data can help create realistic scenarios for testing and fine-tuning your online fraud detection systems.
In addition, machine learning models trained on AI-generated data can detect fraudulent activity more accurately, reducing false positives and negatives. This leads to more efficient fraud detection and less impact on customers’ legitimate transactions.
Data and Information Privacy
The use of synthetic data makes it possible to create shareable information instead of customer data that cannot be shared due to data protection laws. For example, synthetic customer data is ideal for forming machine learning models, which help determine whether a customer is eligible for a credit or mortgage loan and how much is the amount that can be offered.
Risk Management and Regulatory Compliance
Establishing a risk management plan is essential to managing liquidity and functioning properly, as well as taking steps to preserve profitability. Generative AI is a solution that minimizes these resulting losses.
For example, the Generative Adversarial Network (GAN) – a generative model based on deep neural networks – allows you to calculate the value and potential amount of a loss of value at risk for a given period. It also creates economic scenarios to predict the future of the financial market.
Generative AI also helps maintain regulatory compliance. Through process automation, it helps identify potential nonconformities and mitigate risks before they occur; with real-time monitoring of transactions, identification of anomalies and detection of patterns that indicate possible violations. In addition, you can analyze regulatory changes and update systems, ensuring continued compliance with evolving requirements.
Personalization of the customer experience
Delivering optimized, agile and unique experiences is increasingly important in today’s financial solutions. Customers expect customized solutions that meet their individual needs and preferences. Offering a value proposition fosters trust and loyalty, strengthening relationships with people.
Generative artificial intelligence makes it possible to offer personalized financial advice, leveraging data, consumption patterns and customer preferences. By analyzing large sets of information, such as transaction history and financial goals, AI algorithms can generate personalized recommendations tailored to each customer’s unique circumstances.
Another example is investment portfolios. Generative AI can optimize asset allocation and suggest suitable investment options by analyzing historical market data and applying advanced algorithms. This personalized approach ensures that people receive recommendations aligned with their goals, increasing the likelihood of achieving the desired results.
This allows clients to make more informed decisions about budgeting, saving and investing, improving their financial well-being.
Generative artificial intelligence has a major impact on the financial industry with process automation, generation of standards and predictive models, better risk assessment, fraud detection, and the optimization of personalized experiences in the customer journey.
The adoption of this type of technology allows financial services institutions to make data-driven decisions, improve productivity and operational efficiency to remain competitive in today’s digital economy.
How Topaz can help
We use generative AI in several solutions, one of them is Online Fraud Detection, a market-leading anti-fraud solution that helps companies and their users prevent fraud in an innovative and efficient way in transactional channels, digital services and e-commerce. Thanks to the connected and collaborative intelligence ecosystem, we use transactional, biometric, and risk-based authentication machine learning, allowing us to analyze transactions in real time that prevent fraud before it happens.
Get to know our digital channels, anti-fraud and cybersecurity solutions that alleviate the operability of your business, automating complex processes, ensuring efficiency, business competitiveness and increasing the satisfaction of your customers.