AI Research Paper - BFSI

TTEC helps strike the right balance of expert support, proactive solutions, and workforce improvements

TTEC AI RESEARCH SERIES

AI/CX value chain analysis: Bank loans TTEC research uncovers what to automate, augment, and keep human across the bank loan customer journey

RESEARCH REPORT

Overview

When it comes to AI, there is a lot of hype around how the technology will seismically shift customer experiences and the contact center. Both customers and associates are being promised new and easier ways to engage – with AI-powered chatbots, speech analytics, associate assist, and other tools. AI’s potential impact in the CX space reaches customer care, sales, tech support, back office, trust and safety, collections, content moderation, ops/data annotation, and order fulfillment, just to name a few.

With so many AI avenues to explore across the customer and associate lifecycle, where should a CX organization start? We recommend starting with the customer.

Look at the customer interactions producing the highest contact volume or costing the most, then determine if AI will help improve the experience without breaking the bank. Look at where you should use AI, not just if you can. To help determine the best uses of AI in the contact center, TTEC launched a research series examining some of the most common customer journeys across industries. Looking at millions of customer interactions from hundreds of clients, our research team conducted extensive value chain analyses to see what parts of the customer journey should be fully automated with AI, augmented to improve human interactions, or remain untouched by AI and automation. The research provides a roadmap for where AI will have the biggest potential return for both the enterprise and customers. This research paper examines the loan application process in the retail banking industry . Drilling down into six CX value chain steps across four stages of a bank loan application, we uncovered the optimal areas to automate, augment, or invest in humans. From the research, we determined that loan providers should implement AI tools most during the processing stage of the bank loan application journey, and least in the closing and post-closing stages. And for now, AI for associate augmentation is a lender’s best bet. Read on to find out why and how.

TABLE OF CONTENTS

How AI can help the loan process..........................................................................................4

AI for 4 loan journey stages....................................................................................................6

AI/CX value chain analysis......................................................................................................8

ROI analysis...............................................................................................................................11

The future of AI: Assisting humans at moments of friction........................................... 12

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RESEARCH REPORT

How AI can help the loan process

associates, and some are best left to human intelligence. The team drilled down into the stages of the application process and mapped AI recommendations along the CX value chain, based on guidance from Figure 1.

Considering costs and benefits to the business, customers, and the effort versus return, TTEC researchers created a matrix of AI hot spots financial brands can implement within the loan customer journey. Some areas can be fully automated, some augmented to support – James Bednar, VP of Product Innovation, TTEC “

This type of experience – complex, data-heavy, full of manual processes and friction – creates the perfect environment for an AI upgrade. But AI is not an all-or-nothing decision. There are nuances to consider about what types of AI work best in individual parts of the journey (see Figure 1). Figure 1: When to use AI to automate, augment, or invest in humans The chart provides general guidance on when AI-powered full automation or human-centered augmentation is best, plus when to keep the task in full control by people.

To launch the AI research series, we started with a common customer journey that is also ripe for an AI boost: the bank loan application process. According to the Federal Reserve, bank and credit loans are at an all-time high , with more than $12 trillion in loans and leases outstanding in the United States alone as of mid 2023. Anyone who has ever applied for a loan knows it is a manual, time-consuming, complex process, with layers of paperwork for financiers, lawyers, underwriters, and more. Highly sensitive data is shared across many groups, opening the potential for error or fraud. And since money is involved, it’s an emotionally charged experience for most borrowers.

“If it’s about my money, my health, or my kids, I want to talk to a real person.”

When to Automate (for customers or employees)

When to Augment

When to Invest in humans

When you need clear information that does not require interpretation

When you need clear information that requires some interpretation

When you need interpretation that does not require clear information

• Data is easily available and accurate

• Consolidate multiple information sources

• Provide context to a situation

• Static processes that don’t change

• Find information fast

• Act based on social cues or intangibles

• Choices must be made with the data

• Data is easily available in a single source

• Emotionally charged decisions

Source: TTEC

4 | AI/CX VALUE CHAIN ANALYSIS: BANK LOANS

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RESEARCH REPORT

AI for 4 loan journey stages A typical loan application customer journey is comprised of four stages: processing, underwriting, closing and funding, and post-closing (see Figure 2). Prospective borrowers submit an application form, the lender reviews their finances and assesses risk, they receive a specific offer based on their risk, accept it, then close the loan and receive the money. Once the loan is closed, they receive follow-ups from the lender about payment terms and begin a new journey as a paying customer.

AI tools can be deployed across the four stages. Here are common AI solutions for each unique part of the loan journey.

Processing: This stage is very data-heavy with application forms and requests for detailed information. There is clear information with little need for human intervention. It’s the stage most primed for AI automation. Internally, AI tools can extract and validate data, verify loan completions, correct errors, and power real-time crediting decision-making to increase accuracy and speed at the beginning of the customer journey. Externally, virtual assistants can walk borrowers through steps and confirm accuracy and completeness of information to prevent delays. Underwriting: This stage is all about risk, which is again driven by data and clear rules. Here is where some decisions need to be made based on unique variables, so associate augmentation works better here in general. Customer-facing activities are minimal. AI tools serve employees here with AI-enabled risk modeling and scoring, decisioning support, and further verification, validation, and reporting. Closing: Here’s where the money comes in, so customers will want to know there are people behind the information for peace of mind, support, and trust. It’s imperative that companies provide layers of support and communication in this stage. Processes diverge from repetitive tasks to more personalized, unique interactions, making full automation unrealistic.

AI tools in this stage focus on optimizing the repetitive, impersonal tasks like compliance checks, document preparation, loan pricing and optimization, and data integration with title, escrow, and settlement providers. Customer interactions and explanations are best left to human associates to guide customers through the closing process. Post-closing: In the final stage, borrowers get their money, the deed is recorded, and title policies are issued. There is no room for error in this important stage, and the potential for extremely harmful fraud is high. People need to stay at the forefront of this stage, with minimal AI support through loan package completion, post-closing audit reports, and document archiving. Even in AI’s early days, there are AI solutions specific to the loan journey available. They include: Blend, DataRobot, FICO, Fiserv, Kasista, nCino, Socure, Upstart, and Zest AI (note: TTEC does not specifically endorse any of these vendors).

Figure 2: The 4 stages of bank loan application journey A typical loan application has four stages, each with opportunity to implement AI tools in support of the work within the stage.

Closing and Funding

Processing

Underwriting

Post-closing

• Review closed loan package • Review final copies • Underwriter decision review • Review closing conditions and instructions

• Evaluation of

• Regulatory

• Re-review loan package • Prepare closing package • Final conditions review • Pre-closing QC • Execution of

completeness of loan application

compliance review

• Verify credit

• Verify credit worthiness • Order third-party services like credit reports

worthiness and risk scores • Verify character, capacity, and collateral

“Feedback loops are a critical component of any augmentation. Being able to

closing documents and funds disbursement

incorporate the success or areas for improvement not only improves the AI running the processes, but also builds user confidence. This is the key to gaining adoption of technologies that augment – otherwise, associates and

AI Work

• Risk modeling and scoring • Data verification • Reporting

• Document preparation • Compliance checks • Data integration

• Document archiving • Loan package completion • Audit reports

• Data validation • Integration with other systems • Virtual customer assistants

employees will ignore or find a way around what’s in place.”

– Aaron Schroeder, Director of AI Solutions, TTEC Digital

Source: TTEC

6 | AI/CX VALUE CHAIN ANALYSIS: BANK LOANS

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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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“

RESEARCH REPORT

ROI analysis

“The orchestration of human and artificial intelligence coupled with financial industry insights is a beautiful symphony. We can empower ourselves as consumers and empower those on the front line who support financial wellness experiences in every interaction.”

The Return on Investment (ROI) resulting from an AI implementation within the loan journey depends on variables such as:

• Number of underwriters • Number of processors • Number of support staff

• Loan application volume • Funded loan volume • Number of loan officers

Overall, we determined that there are significant efficiencies and cost savings throughout the value chain (see Figure 4). We estimate that automation and augmentation may provide up to 25% incremental ROI , and revenue growth of over 10% for banks and other lenders, depending on the size of the organization.

– Kristen Hein, Global Head, TTEC Banking and Financial Services Practice

Figure 4: ROI value levers Financial institutions can see efficiencies of speed, scale, and cost throughout the loan journey by leveraging AI at key moments of truth.

200 hours approx.

Hours of manual labor saved daily

Reduction by 40%

Cost of loan origination

Reduction by 80%

Loan origination time

20% incremental

Loan origination volume

Reduction by 50%

Loan processing duration

Reduction by 70%

Document collection and setting duration

$5M approx. also depends upon the financial entity Reduction by 50%

Estimated cost savings

Overall operational cost

ROI generated

20-25% incremental

Revenue growth

10.70% Incremental

Source: TTEC

Beyond just a cost discussion, ROI can be achieved by improving qualitative metrics including customer satisfaction, customer retention, cross-sell/upsell, and customer loyalty.

10 | AI/CX VALUE CHAIN ANALYSIS: BANK LOANS

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RESEARCH REPORT

Conclusion

Artificial intelligence shows promise across the world of customer experience. Understanding how best to use it across the CX value chain provides brands with guidance on where to begin their AI journey. For financial services companies, using AI to augment inefficient processes across the lending journey is the best place to start to see ROI without risking customer or employee trust. Stay connected with TTEC for more research on how AI impacts the CX value chain in popular industries and customer journeys. And learn more about making your customer experience AI-ready at www.ttec.ai.

The future of AI: Assisting humans at moments of friction

To win the war for talent and meet customer needs, financial brands must strike a careful balance by focusing on customer expectations and prioritizing employee experience (EX). We recommend investing in what matters to these employees: competitive wages, work-life balance, and a culture that matches their values. Increased costs in these areas can be offset by AI automation in others. As AI tools become more ubiquitous across the CX value chain, people will be a brand’s differentiator in the long run. Don’t count them out.

TTEC’s research shows that AI will not replace people along the loan application journey. In fact, AI has the biggest impact when the tools help people do their jobs more quickly with less friction. This optimized way of working leads to an easier loan process, happier customers, and the ability to handle more volume with the same number of resources. This also means that investment is needed in the human workforce to develop associates with technical knowledge, financial services expertise, soft skills, and empathy. AI is shifting hiring needs from tier 1 associates to more advanced associates who can help customers with more complex, technical, or emotionally charged interactions.

12 | AI/CX VALUE CHAIN ANALYSIS: BANK LOANS

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TTEC Holdings, Inc. (NASDAQ: TTEC) is a leading global CX (customer experience) technology and services innovator for AI-enabled digital CX solutions. The Company delivers leading CX technology and operational CX orchestration at scale through its proprietary cloud-based CXaaS (Customer Experience as a Service) platform. Serving iconic and disruptive brands, TTEC’s outcome-based solutions span the entire enterprise, touch every virtual interaction channel, and improve each step of the customer journey. Leveraging next gen digital and cognitive technology, the Company’s Digital business designs, builds, and operates omnichannel contact center technology, conversational messaging, CRM, automation (AI / ML and RPA), and analytics solutions. The Company’s Engage business delivers digital customer engagement, customer acquisition and growth, content moderation, fraud prevention, and data annotation solutions. Founded in 1982, the Company’s singular obsession with CX excellence has earned it leading client NPS scores across the globe. The Company’s 64,400 employees operate on six continents and bring technology and humanity together to deliver happy customers and differentiated business results. To learn more visit us at ttec.com . About TTEC

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