AI Research Paper - BFSI

RESEARCH REPORT

AI/CX value chain analysis There are a lot of AI options throughout the bank loan journey. So where should financial services companies invest to best optimize the loan application process?

Here’s how it breaks down in specific parts of the CX value chain:

Talent acquisition: AI can undoubtedly enhance the efficiency of hiring talent for critical underwriting roles, such as job application review and information verification. However, a human touch and expertise remain critical for complex decision-making relating to assessing a candidate’s soft skills, candidate adherence to regulatory requirements, cultural fit, and other facets of employment. We recommend using AI to augment human expertise for these activities. Learning and performance: AI can be used to provide new employees with interactive learning modules that cover the basics of loan underwriting, such as the different types of loans, the factors that are considered when underwriting a loan, and the different underwriting methods. Seasoned employees can complete new training modules with AI-powered lessons as well. However, AI cannot yet replace the need for human trainers to provide guidance and feedback to new hires. Human trainers can help them understand the complex concepts involved in loan underwriting, and they can also help new employees develop the critical thinking skills that are essential for making sound underwriting decisions. They can also provide emotional support for a new employee’s first few months on the job. Therefore, we recommend using AI to augment human expertise for new employees, then invest in humans to ensure a smooth closing and funds distribution. Technology integration: As AI continues to develop, integrating technology into existing platforms is becoming more automated. AI-generated APIs and other code is growing in use within integration activity. So while AI can help generate code, we recommend investing in humans

to execute the integration of technology systems across the value chain to maintain accuracy and optimization. Quality analysis: As a loan moves through the process, decision-making gets more complex. In the closing stages especially, QA assesses complex associates’ decision-making, nuanced evaluations, and considerations of various factors related to individual customers, which makes it difficult to completely replace human judgment with AI. We recommend some augmentation to assist associates at the processing and underwriting stages, but invest in people in the closing stage, where personalized attention is important. Customer engagement teams: AI-powered virtual assistants and chatbots can play a valuable role in handling routine customer inquiries, providing quick responses, and automating certain aspects of customer engagement. But when it comes to solving complex problems they often work best in conjunction with human associates. In a high-pressure situation like securing a loan, we recommend using AI tools to augment customer engagement teams early in the process and maintain a human-centric approach in later stages. Workforce management: AI can intelligently route loan applications to the most appropriate underwriting associates based on their skills, experience, and workload. AI-powered systems can provide underwriting associates with decision support tools that leverage machine learning algorithms. It’s a perfect application of using AI to both augment and automate workforce management for key activities to streamline the customer journey.

Overall, the stages move across the continuum from more AI to less AI as the processes shift from generic information gathering to acting on individual insights. While there is opportunity at each stage to use AI tools for individual tasks, Figure 3 highlights where in the bank loan application journey TTEC recommends lenders invest at an enterprise level to generate immediate impact through automation, associate augmentation, or deeper human intelligence. The calculation is based on AI readiness within the business unit and the maturity of AI tools available.

At each stage of the journey, six steps make up the CX value chain in the contact center, from talent acquisition, employee training, and technology integration to customer engagement, QA, and workforce management. Some of these steps are natural fits to implement AI to reduce costs, improve efficiency, and boost satisfaction. Others are best left to people if they are empowered and have the right tools at their disposal.

Figure 3: AI hotspots in the bank loan application process Where to invest in AI automation, augmentation, or people in the bank loan customer journey based on current cost/benefit analyses. +

Processing

Underwriting

Closing and Funding

Post-closing

Pre- qualification (KYC/KYB)

Loan application review

Underwriting process

Underwriting quality control

Funds distribution

Closing

Post-closing

Work Complexity

Medium

Low Extremely high

High

Extremely high

High

Low

Talent acquisition

Augment

Learning and performance

Invest in humans

Augment

Automate

Technology integration

Invest in humans

Quality analysis

Invest in humans

Augment

Customer engagement terms

Invest in humans

Augment

Workforce management

Augment

Automate

Augment

Source: TTEC

8 | AI/CX VALUE CHAIN ANALYSIS: BANK LOANS

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