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Improving customer experience in financial services with generative AI and partners

Improving customer experience in financial services with generative AI and partners

What role generative AI (GenAI) and partners play in Client Lifecycle Management (CLM) in financial services

Published Jan 17, 2025
Last Modified Jan 24, 2025
In this article, we will cover customer trends and the role of GenAI in Client Lifecycle Management (CLM) in financial services in Asia Pacific and Japan (APJ). We will also see how Fenergo, an AWS partner, helped financial institutions improve customer experience.

Trends

According to Mckinsey, by 2026, 81% of banks are planning to adopt AI/ML to improve customer service. There are two trends driving this adoption.
Customers want frictionless experiences along the customer journey with an integrated approach to digital and human interactions. According to Mckinsey, "a typical regional bank has over 1,500 customer journeys (across business units, product lines, and customer interactions." Simplifying and digitizing these journeys as well as leveraging AI through CLM solutions are key factors in reducing friction and improving customer satisfaction.
Due to competition from digital banks, incumbent banks need to reduce their customer acquisition cost (CAC) which includes sales & marketing expenditures and other related expenses to acquire a customer. CAC is a key performance indicator for financial institutions as it impacts their profitability. Customer acquisition cost in retail banking for traditional banks is much higher than for digital banks. Through the use of solutions and partners, one digital bank in Singapore managed to achieve acquisition costs which were 87% lower than traditional models while new user setup took 3 minutes.

Role of AI & GenAI in CLM

AI and GenAI play an important role during the customer lifecycle journey. During the acquisition phase, Gen AI is used to automate customer prospecting and research. A Japanese financial institution used Amazon Bedrock and Amazon SageMaker to gain a better understanding of prospective customers and generate draft presentations for their sales teams.
During onboarding, GenAI is used to provide a seamless experience by simplifying and accelerating customer onboarding & KYC document checks through Intelligent Document Processing (IDP).
When servicing customers, both AI and GenAI are used for fraud detection and transaction monitoring. Gen AI is also used in contact centers to analyze customer sentiment and provide operational feedback from call center transcripts to improve customer experience.
During upselling and cross selling, Gen AI is used to generate hyper-personalized content generation and recommendations for marketing cross-sell and up-sell opportunities. For example, banks can leverage AI to identify buyer and seller relationships of their business customers and then run personalized campaigns to target them.

Implementation considerations

When implementing Generative AI in customer lifecycle management in financial services, there are several important considerations.
The first one is model selection and customization. Choosing appropriate foundation models (FMs) depends on the use case. Models then need to be fine-tuned on domain-specific data for better performance. Amazon Bedrock is a fully managed service that makes high-performing FMs from leading AI companies and Amazon available through a unified API. Amazon Bedrock allows customers to privately customize them with their data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG).
The second consideration is data security and privacy. Customers need to analyze how they access foundation models, how they fine-tune them and how they conduct inference on these models. Amazon Bedrock uses model deployment accounts which model providers don't have access to. Amazon Bedrock will perform a deep copy of a model provider’s inference and training software into those accounts for deployment. As model providers don't have access to those accounts, they don't have access to Amazon Bedrock logs or to customer prompts and completions.
The third one is implementing AI responsibly. Customers can use Amazon Bedrock to create multiple guardrails and apply them across multiple FMs. Customers can configure content filters and word filters to help block harmful content and words. Customers can also define denied topics. For example, a banking application can use guardrails to help block content related to seeking or providing investment advice. Customers can configure sensitive information filters to help block or mask sensitive information. A call center application can use guardrails to redact users’ personally identifiable information (PII) to protect user privacy.
Finally, customers should consider using partners for leveraging GenAI in CLM. One AWS partner who has integrated GenAI into their CLM solution is Fenergo.

Partner solution

Fenergo is an AWS partner that empowers financial institutions and other organizations to fight financial crime. Fenergo provides solutions that digitally transform their client and counterparty experiences, improve operational efficiencies and return on investment, manage compliance with KYC, AML and other regulatory compliance obligations, and reduce risk with continuous monitoring of clients, counterparties and transactions throughout the client lifecycle.
Fenergo leverages Amazon Bedrock for intelligent document processing (IDP) during client and counterparty onboarding and due diligence. Fenergo’s IDP reduces manual document handling by 72% for a typical corporate onboarding case by automating document management tasks such as data extraction as well as document classification, splitting and auto-linking.
Fenergo also leverages Amazon Bedrock for its significance engine, which allows their Client Lifecycle Management system to identify when a field has changed value and to then determine the significance of that change. For example, if an individual changes their place of residence from Spain to Portugal, the CLM system will detect that the value of the "place of residence" has changed, and the Significance Engine will recognize that, due to the geographic proximity, relatively similar economic standings and frequency of such a change, the change will be marked as insignificant and therefore no action must be taken in the journey.

Conclusion

With the pressure to reduce customer acquisition and servicing costs as well as provide frictionless experience to their customers, financial institutions are increasingly leveraging AI and GenAI in CLM through partners.
By integrating Amazon Bedrock and Amazon SageMaker into CLM solutions, AWS partners like Fenergo are improving how financial institutions onboard and service their customers, process documents, and manage customer data. Ultimately, these efficiency gains and solutions provided by Fenergo are helping financial institutions to improve customer experience and fight financial crime.
 

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