Generative AI (GenAI) is revolutionizing financial services with the promise of significant value generation and operational efficiencies. McKinsey projects GenAI could contribute up to $340 billion annually in banking, while EY reports that 78% of financial services executives are actively implementing or exploring GenAI. Both underscore the rapid adoption of GenAI and its transformative potential in reshaping the industry landscape.

Calibrating challenges in the financial services sector/industry

The financial services industry, however, faces a dual challenge in scaling AI/GenAI use cases:

  1. Selecting use cases and proof-of-concept (POC) pilots that can scale effectively is pivotal. Despite numerous GenAI experiments, Everest Group predicts that approximately 90% of POCs will not move into production any time soon, if ever. This could be due to a number of reasons such as stringent regulatory environments, technical constraints, high customization costs, or cultural resistance within traditional financial institutions.
  2. Assessing the value generated by these initiatives poses difficulties. Many financial services executives struggle to quantify the benefits of new technologies. A PWC report reveals that 51% find this task challenging, with 43% citing adoption costs as a major hurdle.

Understanding which use cases offer the most value and can scale effectively is essential for realizing the anticipated economic benefits from AI investments.

Four avenues for a GenAI-powered transformation

In my vast experience of guiding large financial institutions from exploration to implementation and unlocking AI value as a leading member of Eviden’s GenAI acceleration program, I would like to emphasize four key areas with substantial and rapid ROI potential. Beyond empowering employees with tools like Copilot, these can drive transformative impact.

  1. Enable frictionless experiences.

Integrating GenAI technology into financial processes promises significant value by streamlining the delivery of products and services to customers. This includes enhancing new customer acquisition, loan and insurance underwriting, and enriching digital interactions through conversational agents and chatbots.

For example, GenAI can be integrated across the insurance underwriting process to significantly improve new insurance policy purchase experience for customers while reducing administrative burdens for underwriters.

Practical implementation shows it can lead to substantial business improvements such as 30-40% enhanced efficiency, 60-70% faster quote bind turnaround times, and a 25% increase in quote –to-bind ratios.

  1. Implement smart operations.

AI and GenAI can also quickly improve back-office operations by automating repetitive tasks like data entry and compliance checks, reducing errors and manual intervention. GenAI systems can streamline document processing, data reconciliation, and provide instant customer support through chatbots.

For instance, in insurance claims processing, GenAI efficiently automates claims intake, prioritizes complex claims, accurately assesses damage, detects fraud, and enhances customer communication. Through these innovations, leading insurers have achieved up to 25% improvement in claims settlement time, greater accuracy, and enhanced customer satisfaction. Call center operations too can rapidly transform with GenAI, delivering efficiency improvements of up to 15% through self-service automation. When combined with agent assistance for tasks like after-call work and next-best action recommendations, GenAI can further enhance efficiency by an additional 12%.

  1. Foster security, risk and compliance.

GenAI excels at analyzing transaction patterns to swiftly and accurately detect fraudulent activities in real-time, significantly reducing the false positive rate by 20% and minimizing financial losses through earlier detection.

Additionally, GenAI aids in monitoring regulatory changes, generating precise reports and alerts that ensure institutions maintain compliance and avoid penalties. These enhancements yield tangible business benefits such as improved compliance ratios, comprehensive due diligence coverage, and faster resolution of AML alerts.

  1. Ignite next-gen innovation.

GenAI holds immense potential in transforming financial services through hyper-personalization. Beyond traditional audience segmentation, GenAI empowers financial institutions to understand their customers better, anticipating key life events to engage them effectively at scale. It also facilitates dynamic pricing and AI-driven call centers. By leveraging comprehensive customer insights, businesses can enhance customer engagement, loyalty, and operational efficiency.

For instance, Česká Spořitelna, a leading Czech bank and Eviden client, integrated diverse customer data sources to automate branch agent notifications, resulting in 1000 new client meetings within three months. Additionally, personalized recommendations boosted agent call effectiveness by an average of 50%.

 

Top 3 best practices to enhance AI/GenAI integration

Beyond specific use cases, unlocking the full value of GenAI hinges on three essential best practices gleaned from field experience:

  1. Conduct a comprehensive value chain examination.

Financial institutions should start by thoroughly examining their entire value chains, focusing on product development, customer acquisition, risk management, operations, CRM, and compliance. McKinsey advises a centralized operating model for GenAI initiatives which can help identify improvement areas, prevent siloed projects, and facilitate smoother scaling across the organization.

  1. Identify strategic KPIs.

Pinpointing measurable KPIs is crucial for maximizing AI’s impact. For instance, AI-driven predictive analytics can enhance risk assessment and underwriting accuracy, improving metrics like faster policy issuance, reduced loss ratio, and premium growth. With clear KPIs and a deep understanding of the value chain, financial institutions can prioritize AI use cases for rapid, measurable value creation.

  1. Invest in data platform modernization.

Upgrade data platforms to handle vast data with agility and accuracy, essential for effective AI models. Modern platforms enable real-time data processing for fraud detection, risk assessment, and personalized customer experiences. They support machine learning (ML) integration and AI-driven automation, optimizing back-office operations and enhancing decision-making.

 

Chart a sustainable future with GenAI

The industry is undergoing a fast-paced GenAI-led transformation, advancing so rapidly that keeping pace is imperative. Experiments are underway to enable autonomous enterprises, envisioning financial institutions where AI systems monitor and optimize the entire value chain, autonomously managing operations and customer experiences. Although this vision is distant, it raises critical questions about the path forward.

Financial institutions must accelerate their GenAI-led transformation to stay competitive against agile fintech players. By thoroughly examining the value chain, identifying key performance indicators (KPIs), and investing in modern data platforms, institutions can prioritize high-impact AI initiatives with tangible and sustainable business benefits. Early adopters leveraging GenAI’s transformative power will gain a decisive edge, ensuring they lead the industry rather than follow it. Accelerate your digital transformation with AI, and lead the revolution today.

 

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