{"id":6075,"date":"2024-06-18T07:53:52","date_gmt":"2024-06-18T02:23:52","guid":{"rendered":"https:\/\/blogs.infosys.com\/digital-experience\/?p=6075"},"modified":"2024-06-18T07:53:52","modified_gmt":"2024-06-18T02:23:52","slug":"banking-efficiencies-where-generative-ai-can-provide-efficiencies-with-complaints-handling","status":"publish","type":"post","link":"https:\/\/blogs.infosys.com\/digital-experience\/emerging-technologies\/banking-efficiencies-where-generative-ai-can-provide-efficiencies-with-complaints-handling.html","title":{"rendered":"Banking Efficiencies – Where Generative AI can provide efficiencies with Complaints Handling"},"content":{"rendered":"

Why Banks must prioritize efficiencies for Complaints Handling to address Customers Complaints and to retain Customers<\/h2>\n

\"\"\"AI<\/h2>\n

Overview<\/h2>\n

Banking organisations invest GBP millions in creating, marketing, implementing, and supporting their product portfolios; the aim to make Banking products attractive and competitive for customers’ requirements, whether it is a Share Dealing Account<\/strong> for a professional interested in shares and stocks, a Current Account<\/strong> for an employee or a Savings Account<\/strong> for a child.<\/p>\n

Banks provide a customer support center, a helpdesk to handle a variety of enquiries including complaints handling. A complaints journey can an emotional one for the customer, as well as for the handler who is responsible for ensuring the complaint is addressed as effectively as possible from inception to resolution<\/strong>.<\/p>\n

Business Challenges with Complaints Handling<\/h3>\n

Complaints are initiated by a variety of communications such as voice, chat messages, emails containing attachments as well as traditional paper-based complaints. The format of complaints is usually unstructured, and this provides the core business challenges as Banking systems have certain difficulties to interpret, classify and extract information.<\/p>\n

Generative AI (Artificial Intelligence) understanding of business case context and the generation of human-like responses, increases the ability to improve the processing efficiency of complaints.<\/p>\n

\"\"<\/h3>\n

Data Privacy and Security Concerns<\/h3>\n

Generative AI models require AI type Policies and Tools<\/strong> to ensure security measures are present to safeguard sensitive data contained within complaints. Compliance with data protection regulations ensures responsible and ethical usage. Generative AI should only be used with Privacy, Accountability, Bias and Fairness, Explainability, Security, Accuracy, Transparency<\/strong> pillars with Human in-the-loop<\/strong>, to verify generated responses.<\/p>\n

Enhancing Natural Language Understanding<\/h3>\n

Large-Language-Models can understand human language and the way that humans write. Incorporating and integrating these with Bank\u2019s Complaints Systems ensures that key issues and context<\/strong> can be interpreted, which results in a better accuracy of information extraction.<\/p>\n

Here\u2019s where Banking Efficiencies can be made<\/h3>\n

Generative AI Complaints models<\/strong> can be trained with suitable datasets and fine-tuned to generate contextually relevant and empathetic responses. Resolution quality improves with a speedy, mature response, whilst maintaining customer communications, for which Banking customers expect.
\nAutomated categorisation and summarisation of text are supported. Categorisation can also aid extraction to trigger business workflows to the correct teams for further responses\/approvals.<\/p>\n

Continuous Learning of Complaints Learning Model<\/h3>\n

Banking Complaints Platforms use built-in feedback learning functionality. Generative AI has the capability to continuously learn using specific datasets. A combination of Generative AI and Banking Complaints Platforms results in improved learning models, accuracy, and efficiency gains with processing of Complaints data.<\/p>\n

Efficiency benefits = Retained Customers and Happy Customers<\/h3>\n

Banking Market Insights<\/h3>\n

\"\"<\/p>\n

JP Morgan<\/strong><\/em> uses AI to categorise incoming messages based on keywords\/themes. AI tooling is utilised to reduce fraud.\u00a0 The routing of messages to correct departments, benefits customers with a speedier settlement, if a complaint related to fraud was initiated<\/strong>.<\/p>\n

Summary<\/h2>\n

Generative AI provides efficiencies for the Complaints Learning process<\/strong> incorporating language understanding, summarisation, and continuous learning capabilities.<\/p>\n

Delays with Banking Go-to-Market solutions for interpreting Complaints based on historical data, due to uncleansed and mixed quality of data affects the interpretation of data. Generative AI can ensure that similar data formats can be generated to meet business timelines.<\/p>\n

The result can be a mature customer service offering, improved customer satisfaction and customer retention based on accurate interpretations from complaints information and insights.<\/p>\n

Infosys\u2019s Digital Experience (DX) unit\u2019s Consulting arm<\/strong> can help drive Thought Leadership<\/strong> conversations with existing clients. Infosys DX\u2019s Consulting<\/strong> domain knowledge, experience and understanding of Financial Services can help open conversations and support initiatives towards providing best-of-breed Generative-AI based offerings.<\/p>\n

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References:<\/h4>\n