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Author
Saurabh Maur
Saurabh Maur
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Generative AI has the potential to revolutionize the banking industry for an additional value of $200 billion to $340 billion annually. While the full potential of GenAI in the banking industry is still being explored, this technology is rapidly revolutionizing how banks operate for an additional value of $200 billion to $340 billion annually. So far, Generative AI is fast building unprecedented customer experiences with chatbots and virtual assistants. It is automating repetitive tasks such as processing paperwork and answering customer questions and is helping businesses identify and mitigate risks, such as fraud and cyberattacks.

To help you understand how Generative AI is used in banking in its full breath, this article explores the application models, benefits, challenges, and considerations for a smooth implementon.

Generative AI Application Models – Way of working

Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), are two powerful tools in the field of artificial intelligence. These tools can learn patterns and structures from training data and generate new data that shares similar characteristics. Let’s take a real-life example of Generative AI in banking is using GAN model to generate synthetic data for fraud detection. GANs consist of two neural networks, a generator, and a discriminator, that work in tandem to generate realistic synthetic data. The generator's role is to create synthetic data, such as images or text, while the discriminator's task is to differentiate between real and generated data.

GenAI can improve the accuracy of fraud detections systems for banks, by:

  • Creating synthetic data sets that mimic real-world transaction patterns, including legitimate and fraudulent activities. These synthetic datasets can then be used to train fraud detection models.
  • Mitigating the challenges associated with limited or imbalanced fraud data that impedes the effectiveness of traditional fraud detection algorithms. GANs enable the creation of large and diverse datasets that encompass various fraud scenarios.
  • Generating new synthetic data and adapting the fraud detection models to the ever-evolving fraud patterns, which also helps in reducing false positives and false negatives.

Generative AI's ability to simulate realistic fraud scenarios and generate synthetic data provides banks with a powerful tool to bolster their fraud detection systems and enhancing their ability to identify and prevent fraudulent activities, while reducing the inconvenience for legitimate customers.

How do Generative AI business models work in banking

Banking institutions around the world can use any of the following three business models:

  1. Model as a Service (MaaS): Companies access Generative AI models through the cloud and use them to create new content. OpenAI employs this model, which licenses its GPT-3 AI model, the platform behind ChatGPT. This option offers low-risk, low-cost access to generative AI, with limited upfront investment and high flexibility.
    For instance: Generative AI for Fraud detection service in banks.
    In this scenario, a technology specialized company does a Generative AI-powered fraud detection for banks and financial institutions. The service uses advanced machine learning techniques to analyze large volumes of transaction data in real-time and identify patterns indicative of fraudulent activities.
  2. Built-in apps: Companies build new or existing apps on top of Generative AI models to create new experiences. GitHub Copilot uses this model, which relies on Codex to analyze the context of the code to provide intelligent suggestions on how to complete it. This option offers high customization and specialized solutions with scalability. Nagarro is currently using this model to create customized product and services and various PoCs for customers using Generative AI models in GAiN platform.
    For instance:
    A personalized Financial Planning App can help you utilize Generative AI to provide personalized financial planning and advisory services to its customers. The app goes beyond traditional banking functionalities and aims to help customers make informed financial decisions based on their unique goals and circumstances.
  3. Vertical integration: Vertical integration leverages existing systems to enhance the offerings. For example, companies may use Generative AI models to analyze large amounts of data and make predictions about prices or improve the accuracy of their services.
    For instance: Vertical Integration for Mortgage Services using Generative AI
    A bank pursues a vertical integration strategy leveraging Generative AI to enhance its mortgage services. The bank expands its offerings to cover the entire mortgage lifecycle, from initial application and underwriting to customer support and risk management, all powered by advanced Generative AI technology. The key components will be Automated Application Processing, Smart Underwriting, AI-Driven Risk Assessment, Personalized Mortgage Recommendations, AI-powered chatbots and virtual assistants.

Application of Generative AI in banking

Generative Adversarial Networks (GANs) and various language models can be applied in various ways within the banking industry to improve customer experiences, optimize processes, and enhance decision-making. Let’s explore some of the common use cases for Generative AI in banking:

1. Fraud Detection and Prevention

Generative AI can play a significant role in improving fraud detection and prevention systems by augmenting traditional rule-based approaches with its ability to recognize patterns and anomalies in the data. Here's how Generative AI can be applied to enhance fraud detection and fraud prevention in banking:

  • Anomaly Detection: As mentioned earlier, Generative AI models such as autoencoders or GANs, can be trained on legitimate transaction data to learn the normal behavior patterns of customers. Once trained, these models can identify deviations or anomalies in real-time transaction data, help flag potentially fraudulent activities. Unusual spending patterns, atypical account behaviors, or transactions from unusual locations can be recognized and brought to fraud analysts’ attention.
  • Synthetic Data Generation: Create synthetic datasets that mimic normal transaction behavior that can be combined with real data to train fraud detection models more effectively. This augmentation enhances the model's ability to generalize to new, unseen fraudulent patterns, making it more robust in detecting emerging fraud schemes.
  • Behavioral Biometrics: Develop behavioral biometrics models to analyze customer interactions with digital banking platforms. By learning from individual user behavior patterns, the system can identify unauthorized access attempts and detect fraudulent activities, such as account takeovers or phishing attacks.
  • Fraud Alert Enhancements: Improve fraud alert systems by reducing false positives and increasing detection accuracy. By recognizing subtle patterns and identifying genuine customer behavior, the system can better distinguish between normal and fraudulent activities, ensuring legitimate transactions are not unnecessarily flagged.
  • Real-time Fraud Prevention: Deploy in real-time to monitor transactions and identify potential frauds in milliseconds. This swift response time allows banks to halt fraudulent transactions as they occur, preventing further damage and protecting customer funds.
  • Adaptive Learning: Continuously learn and adapt to new fraud trends as fraudster tactics evolve. These models also adapt to identify and analyze new patterns and update its algorithms to stay ahead of emerging threats.
  • Transaction Verification: Integrating Generative AI with biometric authentication methods like facial recognition or fingerprint scanning, banks can add an extra layer of security to verify the authenticity of high-risk transactions.
  • Money Laundering Detection: Detect patterns associated with money laundering activities by analyzing transaction histories, customer profiles, and network relationships. The system can flag suspicious activities that may indicate money laundering attempts.
  • Fraud Investigation Support: Assist fraud investigators by providing insights and generating visualizations of complex transaction data, helping them identify connections and hidden patterns among multiple fraudulent entities.

2. Risk Modeling and Prediction

  • Financial Simulation and Stress Testing: Generating synthetic datasets for stress testing and scenario analysis. Generative AI can simulate various economic scenarios and stress test a bank's portfolio to assess its resilience to market shocks and economic downturns. This helps banks prepare for adverse conditions and make more informed risk management decisions.
  • Market Analysis and Predictive Modelling: Simulate market conditions for risk management and portfolio optimization. Banks deal with vast amounts of financial data. Generative AI can be leveraged to analyze market trends, historical performance, and economic indicators to generate predictive models for making investment and trading decisions.
  • Risk Assessment and Credit Scoring: Predictive models for credit scoring and loan default prediction. When evaluating loan applications, banks need to assess the creditworthiness of applicants. Generative AI can analyze a broader range of data sources, including non-traditional data such as social media activity and purchasing behavior, to improve risk assessment models and provide more accurate credit scores.

3. Customer Service and Personalized Finance

  • Personalized Customer Support: Chatbots and virtual assistants for personalized customer interactions. Generative AI can be used to build chatbots and virtual assistants capable of engaging in natural language conversations with customers. These AI-powered agents can provide personalized assistance, answer inquiries about account details, transaction histories, and offer product recommendations based on the customer's financial behavior.
  • Virtual Characters and Avatars: Generative AI models can create virtual characters or avatars that exhibit human-like behavior, expressions, and speech. These characters can be used in virtual reality, gaming, or animation.
  • Natural language generation for customer communication and support
  • Automated Investment Advice: Offer personalized financial investment advice and recommendations to customers based on their financial goals, risk tolerance, and market conditions. It can help customers make informed investment decisions aligned with their individual circumstances.

4. Product Innovation and Design Enhancements

  • Generative design algorithms for creating innovative financial products
  • Automated product ideation and prototyping processes
  • Simulating market demand and predicting customer preferences
  • Customer Sentiment Analysis including analyzing customer feedback, social media posts, and reviews to gauge customer sentiment and identify potential issues or areas for improvement in banking services and products.
  • Design and Creativity: Artists can use these models to explore new ideas, generate visual concepts, or even create interactive installations.
  • Image Synthesis to generate realistic images for instance StyleGAN can generate high-quality images of faces, landscapes, and objects that don't exist.
  • Text Generation to generate coherent and contextually relevant text given a prompt. Language models based on generative AI, such as GPT-3 can be used for tasks like writing articles, generating code, or even creating conversational agents.

5. Document Generation

  • Financial Document Generation: Banking involves numerous documents like contracts, loan agreements, and investment reports. Generative AI can automate the generation of these documents, ensuring accuracy, compliance, and efficiency. This can streamline processes and save time for both customers and banking personnel.
  • Natural Language Processing (NLP) for Compliance: Generative AI-powered NLP models can assist banks in analyzing and understanding complex regulations and compliance documents. This can help ensure that the bank adheres to the latest regulatory requirements and reduces the risk of non-compliance.

Benefits of Generative AI in Banking

Generative AI offers a range of benefits to the banking industry, revolutionizing various aspects of operations, customer experiences, and decision-making processes as discussed above in various use cases. Summarized are the key benefits of using Generative AI in banking:

  • Personalized Customer Experiences (15-20% increase in customer satisfaction)
  • Improved Fraud Detection and Prevention (reduction by 20-30% annually)
  • Enhanced Risk Assessment
  • Automated Document Generation
  • Market Analysis and Predictive Modelling
  • Enhanced Compliance and Regulation
  • Cost Savings and Efficiency (20% increase annually)
  • Real-Time Decision Support
  • Innovation and Product Development
  • Risk Management and Stress Testing
  • Improved Customer Engagement (15-20% increase in customer satisfaction)

Generative AI has the potential to transform banking operations, customer experiences, and decision-making, making it a powerful tool for driving innovation and competitiveness in the BFSI industry by increasing revenues by 15-20% annually.

Generative AI has the potential to transform banking operations, customer experiences, and decision-making, making it a powerful tool for driving innovation and competitiveness in the BFSI industry by increasing revenues by 15-20% annually.

Challenges and Considerations

While Generative Artificial Intelligence (AI) offers numerous opportunities for innovation and improvement in the banking industry, it also comes with several challenges and considerations that need to be addressed:

  • Data Privacy and Security: Banks handle vast amounts of sensitive customer data. A robust data privacy policy and security measures are needed to protect this information from breaches or misuse. The use of synthetic data and privacy-preserving techniques can help mitigate these risks, but it's crucial to comply with relevant regulations and maintain data integrity. Ensuring privacy and protection of sensitive customer data is possible by complying with data protection regulations that can address potential biases in generative AI algorithms.
  • Bias and Fairness: Generative AI models can inherit biases from the data they are trained on, leading to potential discrimination in decision-making processes. For example, biased credit scoring could lead to unequal treatment of customer groups. Banks must ensure fairness in AI models and regularly audit their systems to address and minimize bias.
  • Explainability and Transparency: Many Generative AI models are complex and difficult to interpret. In the banking sector, explainability is crucial to gain customer trust and regulatory compliance. Banks need to invest in technologies that provide insights into how AI systems arrive at their decisions.
  • Ethical Considerations: Ensure fairness and transparency in generative AI outputs; mitigate risks of unethical use or unintended consequences; and establish responsible AI governance frameworks.
  • Regulatory Compliance: The banking industry is heavily regulated, and deploying AI models requires adherence to various legal and compliance frameworks. Banks must ensure that their AI implementations comply with laws like the General Data Protection Regulation (GDPR) and the Fair Credit Reporting Act (FCRA), among others such as compliance with banking and financial regulations, adhering to anti-money laundering and know-your-customer requirements, ensuring compliance with data privacy and consumer protection laws.
  • Model Robustness and Reliability: Generative AI models need to be robust against adversarial attacks and noisy data. In a dynamic banking environment, models must adapt to changes in customer behavior, market conditions, and fraud patterns while maintaining reliability and accuracy.
  • Resource Intensiveness: Training and deploying complex generative AI models can be computationally expensive and require substantial computational resources. Banks need to invest in the infrastructure necessary to support these AI initiatives effectively.
  • Data Quality and Availability: The success of Generative AI models relies on high-quality, diverse, and relevant data. Banks may face challenges in accessing and collecting such data and ensuring it represents the entire customer base.
  • User Acceptance: Customers may be skeptical or hesitant about interacting with AI-powered systems for sensitive financial tasks. Banks need to focus on user education, transparency, and delivering a seamless customer experience to build trust and acceptance.
  • Interoperability and Integration: Integrating Generative AI solutions with existing banking systems and processes can be challenging. Banks must plan for seamless integration to maximize the value of AI investments and avoid disruptions.
  • Continuous Monitoring and Updates: AI models require continuous monitoring to detect performance degradation, bias drift, and adversarial attacks. Regular updates and improvements are essential to ensure the models remain effective and relevant over time.

When implemented responsibly and ethically, Generative AI will reap significant benefits to banks. Addressing these considerations, banks can leverage Generative AI to enhance customer experiences, improve decision- making processes, and further strengthen fraud detection and prevention mechanisms.

Our take

Whether you are going for complete digital transformation of your banking operations or to introducing new enhancement in your existing mechanisms, Generative AI is the way forward.

Nagarro can help you find a way to seamlessly incorporate these models in your existing product roadmaps without disrupting any functions or processes. Our AI Centre of Excellence and in-house PoC labs can give you a good idea on every aspect of GenAI and what use cases are possible for your financial organization.

Our happy customers in the banking and financial domain are a proof of how they integrated AI-enabled advance technologies without making it seem like a monumental task. So, let us show you the future potential for banking that awaits you. Let's talk!