Date published

Apr 30, 2024


The financial services sector stands on the brink of a new digital dawn, with generative artificial intelligence (AI) at its heart. From enhancing customer engagement to optimising back-office operations, generative AI is not just a technological advancement, it’s a strategic imperative. But how can financial institutions harness its potential while navigating the complex innovation landscape? Here, we’ll explain the concept of generative AI and offer a high-level, strategic framework for identifying opportunities and adopting the technology.

What is Generative AI?

Generative AI, which includes technologies capable of producing new, diverse forms of content such as text and data patterns, is poised to revolutionise the financial industry. Its applications range from improving customer service interactions to automating complex document processing tasks. The key lies not only in understanding this technology but also in identifying where it can confer a strategic advantage.

A Broad Range of Financial Services Applications

In financial services, generative AI can streamline processes across a wide range of activities. For example, back office functions like complaint handling have been transformed by AI-driven solutions that classify customer feedback and draft timely, personalised responses. This not only increases operational efficiency but also elevates the customer experience.

Illustrating the breadth of potential applications, AI also plays a role in customer onboarding, where it can expedite identity verification, and in fraud detection systems, where it analyses patterns to pinpoint anomalies. In the predictive analytics sector, it's capable of forecasting market trends and customer behaviour, providing a valuable tool for decision-making.

Case Study: Revolutionising Complaint Management with Generative AI


In the competitive landscape of financial services, customer satisfaction is paramount. A leading UK bank recognized that its complaint handling process was a critical touchpoint in the customer journey, yet it was plagued by inefficiencies, delays, and a growing volume of inquiries that outpaced its resources. Seeking to transform this area, the bank turned to generative AI for a solution.


The bank faced several challenges with its existing complaint management system:

  • Manual processing of complaints was time-consuming and prone to human error.

  • A lack of consistency in responses risked damaging customer relations.

  • The rising volume of complaints put a strain on resources and increased resolution times.


Leveraging generative AI, the bank implemented a system that could accurately analyse content and generate appropriate responses. The process involved several steps:

  • Complaints classification: Using AI, the system categorised complaints by type and urgency, ensuring they were directed to the appropriate teams.

  • Knowledge integration: The AI accessed a repository of resolutions from past cases and regulatory guidelines to inform its responses.

  • Draft generation: The system generated initial drafts for the complaints responses, significantly reducing the time agents spent writing replies.

  • Continuous learning: The AI model refined its understanding of the tone and complexity of complaints over time, producing increasingly sophisticated responses.


The adoption of generative AI in complaint handling transformed the bank's operations:

  • Reduced processing time from hours to minutes, increasing the efficiency of complaint resolution.

  • Enhanced customer satisfaction through faster, more consistent, and more accurate responses.

  • Empowered customer service agents to focus on personalising the AI-generated drafts rather than crafting responses from scratch.

  • Improved the ability to scale and adapt to fluctuating complaint volumes without requiring additional staffing.


The strategic implementation of generative AI in complaint management allowed the bank to not only meet but exceed customer expectations, streamline internal processes, and maintain a competitive edge. The success of this initiative has prompted the bank to explore further applications of AI across its operations, marking a step forward in its digital transformation journey.

Case Study: Enhancing KYC Processes with Generative AI


A multinational financial institution faced increasing pressure to comply with stringent Know Your Customer (KYC) regulations without compromising customer experience. With an expanding global customer base, the manual verification process was becoming unsustainable, error-prone, and negatively impacting onboarding times.


The challenges were multifaceted:

  • Lengthy onboarding processes due to manual data entry and verification.

  • Inconsistencies and errors in customer data handling.

  • Increasing operational costs associated with KYC compliance.

  • Customer dissatisfaction due to delays in account activation.


To address these challenges, the institution implemented a generative AI-powered system with the following features:

  • Document analysis: Using AI to interpret and extract key information from identity documents, reducing manual review time.

  • Risk assessment: AI models assessed customer data against regulatory compliance requirements, streamlining the decision-making process.

  • Narrative generation: The AI system produced compliance reports by summarizing customer information and verification outcomes, ready for internal audits.


The deployment of generative AI in the KYC process brought remarkable results:

  • Accelerated customer onboarding, reducing the average time from days to minutes.

  • Improved accuracy in compliance reporting and reduced operational risks.

  • Substantial cost savings due to a decrease in manual workload and the ability to redirect staff to higher-value tasks.

  • Enhanced customer satisfaction with a swift, hassle-free onboarding experience.


By integrating generative AI into their KYC process, the financial institution not only achieved regulatory compliance more efficiently but also enhanced customer trust and loyalty—a crucial component in the highly regulated financial services sector. This success has led to the broader adoption of AI technologies across other customer-facing and regulatory processes within the institution.

Strategically Implementing Generative AI

The strategic implementation of generative AI begins with an assessment of current processes and identifying opportunities for improvement or innovation. Financial institutions must consider key areas that will benefit from automation or enhanced decision-making capabilities enabled by AI.

Data governance is an essential pillar of this strategy, ensuring that data is handled securely and in compliance with regulatory standards. While risks associated with data privacy and ethical AI use are important, a well-crafted governance framework can mitigate these concerns and build a solid foundation for AI initiatives.

The Strategic Process

Developing a generative AI strategy involves several structured steps:

  1. Audit and Assessment: Begin by evaluating your existing digital infrastructure. Consider any AI or ML tools currently in use and how effectively they serve your organisation's needs. This will help you understand where generative AI can enhance or revolutionise your processes.

  2. Strategic Planning: Define your organisational goals in the context of AI. What are your long-term visions? How can generative AI align with and propel these ambitions? This phase includes outlining both the ethical and safety considerations to ensure responsible deployment.

  3. Identifying Opportunities: Pinpoint key business processes ripe for AI integration. Assess customer journeys and interactions to determine where AI can have the most significant positive impact. This is about prioritising value and feasibility.

Building and Deploying Generative AI Solutions

Once a strategy is in place and opportunities are identified, it's time to move to the practical stages:

  1. Solution Development: Select appropriate generative AI models that meet your identified opportunities. This selection can range from open-source models to proprietary systems, each with its pros and cons.

  2. Integration and Implementation: Integrate generative AI into your existing digital ecosystem, or develop new AI-powered solutions. Ensure these tools are trained and fine-tuned to align with your specific business goals.

  3. Testing and Evaluation: Continuously measure and test your AI models for accuracy and effectiveness. Incorporate user feedback to refine and improve the AI solutions, ensuring they adapt to changing needs and environments.

Addressing Executive Scepticism

It's natural for financial executives to approach new technologies with a degree of scepticism, particularly when considering the rapid pace of change and the potential for disruption. However, generative AI is not a future concept—it’s an evolving reality. Institutions that have incorporated AI responsibly have seen tangible benefits, from cost savings to improved customer loyalty.


Generative AI is here - it's an actionable present, reshaping how financial services operate and compete. Whether your institution is looking to refine customer experiences, bolster security measures, or transform data management, the road to AI adoption should be navigated with a strategic and informed approach.

Are you ready to embark on this journey and harness the power of generative AI? Contact Creode today to take the first step towards unlocking the potential of AI for your financial services organisation. Our team of experts is on hand to guide you through each stage of strategy, identification, and implementation, ensuring your transition to generative AI is smooth, strategic, and aligned with your core business objectives.