Traditional consumer lending firms are struggling to maintain their market share due to new relatively slow growth and stiff competition from new, digital native start-ups. Non-bank fintech companies have expanded to gain a significant foothold in the lending market, and are growing rapidly.
Digital adoption has been a major driver of these trends and is continuously transforming the nature of lending. Innovating and adopting digital solutions has helped fintechs to reduce turnaround times for going from loan application to funding to as low as 24 hours, manage operational costs, and drive sustainable profitability.
In contrast to these advancements, many traditional banks are still dealing with high cycle times, taking more than a month to issue products such as home equity loans or mortgages. This correlates with a dependence on manual operations and the non-integrated legacy system environment often found at banks.
Banks are aware that digital innovation is a key driver for enhancing business performance through differentiating customer experience. While this has resulted in a drive for financial institutions to digitally transform how they interact with customers and offer products, digital adoption has been slow paced due to failed attempts and a lack of results.
This paper provides insights on the major pain points in loan processing lifecycle that must be addressed, the associated challenges, potential areas for digital transformation, how digital solutions can drive sustainable growth, and how an outcome-lead approach quickly realizes benefits.
Pain points that banks are facing:
- Long cycle time from loan application to funding, resulting in poor customer experience
- High customer acquisition costs due to manual processing
- Increasing withdrawal and decline rates due to inefficient product alignment and poor customer experience
- Error-prone, manual data maintenance and reporting, creating risk for compliance violations and fines
- Inability to scale up for seasonal demands without heavily impacting the cost to serve
- Scattered and duplicate information across systems working in silos and lack of integrated workflow
- Reduced production margins due to pricing competition
- Inability to keep pace with the changing customer needs
Multiple digital interventions can be brought in at various stages to improve the overall loan processing lifecycle. The transformed operation delivers a customer-centric approach, and an improved human and machine interface brings continuous improvements for CX/NPS, revenue growth, ability to scale, and risk reduction across the organization. Banks should consider transforming several areas and phases of the consumer lending process to achieve these goals.
- Intelligent voice recognition (IVR), embedded analytics, chatbots, and natural language processing (NLP) offer significant optimization potential by reducing call volumes, average handle time, and providing an enhanced omnichannel customer experience.
- Advanced analytics with deep learning models can optimize product alignment during initial sales to improve pull-through rates, enhance the sales funnel, and cross selling.
- Robotic process automation (RPA) can be brought in at various stages during initiation, including KYC checks, data aggregation, initial review, application data entry, and workflow optimization for inter-departmental follow-ups.
- Optical character recognition (OCR), intelligent character recognition (ICR), and text mining and sensitivity analysis can transform data extraction and other mailroom operations. Coupled with RPA, this can significantly reduce cycle times and operational costs.
Underwriting - Review and Decision
- Leveraging OCR/ICR, automated information extraction, RPA, and embedded analytics for checking customer credit worthiness can bring significant efficiency and accuracy improvements when rendering a loan.
- Machine assisted-decision making based on risk profiles offers the potential for significant optimization during the underwriting process resulting in high-quality reviews, fast turnaround, and fewer defaults and non-performing loans.
- Leveraging RPA to create single customer view for underwriters can reduce manual efforts of extracting data from multiple scattered systems
Closing and Funding
- RPA can eliminate manual interventions for data reconciliation, loan package creation and review, and customer communications.
- RPA integrated with business process management (BPM) tools and existing systems can automate high volumes of payment processing transactions, daily balancing, and reconciliation activities.
- RPA coupled with NLP and OCR/ICR can eliminate human interventions for data maintenance and data update activities.
- RPA/BPA can optimize escrow management and default servicing.
- NLP and machine learning (ML) algorithms can significantly reduce in cycle times and manual efforts for paper and email client communications. These4 communication can be automatically classified, routed, and, in some cases, even auto-responded.
- Predictive analytics can identify potential default scenarios for proactive intervention.
- RPA can reduce manual effort and improve efficiency in validating loss mitigation, case management, and client communications.
Why are most digital initiatives failing?
Like any initiative, digital transformation comes with its own risks, issues and challenges. Digital initiatives have failed to deliver their expected benefits due to some frequently encountered pitfalls. By being aware and accounting for the following challenges, organizations can have a better chance of seeing digital success.
- Embarking on digital transformation for the right reasons with the right approach: Digital initiatives will not be successful is they take an IT-exclusive approach. This lack of consideration for cohesive business strategy has resulted in poor outcomes for businesses and their clients. It is critical to implement a design thinking approach based on generating the right context from data and domain expertise and leverages various interventions to orchestrate a solution design that results in tangible, impactful, and achievable outcomes.
- Fix broken processes before digitally transforming: Digital solutions are ineffective if the underlying processes are suboptimal or broken. Automating a poor process will result in a bot committing the same errors, only faster. In order to reduce operational risks and to get the desired benefits, organizations should consider improvement initiatives such as process simplification, standardization, and lean enhancements before introducing digital solutions.
- Man-machine harmonization is critical: Although integrating human and machine workers should be the first step during any digital transformation journey, many banks fail to incorporate it due to oversight or a lack of agility. In the digital world, man-machine harmony is key to success. The human workforce must be appropriately trained to manage processes that utilize RPA, ML or AI techniques, and a plan must be put in place to manage the digital workforce. A well-planned change management strategy is crucial before diving into digital transformation.
- Digital brings new risk and compliance challenges As processes such as loan origination, underwriting or servicing take advantage of RPA, ML, and AI, a unique set of challenges arise within the risk and control environment. The digital workforce must appropriately complement human workforce without compromising operational risk or regulatory compliance. Bots must maintain, or even improve, the level of auditability and risk and control capabilities that existed prior to the transformation. Failure to pay attention to digital regulatory and compliance implications could be devastating.
The EXL approach for reimagining consumer lending
EXL’s digital consumer lending solution provides banks with an approach and set of digital solutions that transform the retail lending process end-to-end, from loan origination, to servicing, to default processing. This enables retail lending organizations to take a comprehensive, outcome-driven approach towards enhancing customer experience, speeding up loan origination and servicing, reducing manual intervention, and improving process quality. This results in improved competitiveness and revenue growth.
EXL’s digital building blocks for accelerated retail lending transformation
EXL has a series of proven digital solutions that can be leveraged within various digital lending process steps, depending on the organization’s current landscape. These solutions include:
- Data Extraction Bot: Uses structured or unstructured documents with OCR/ICR implementation for data extraction to gather data from loan applications and supporting documents such as drivers licenses, utility bills, and other sources
- Auto Data Entry Bot: Automated data entry from source documents to processing systems, such as customer master and account set-up
- Rule Engine Framework and Rules Bot: Cognitive rules engine to drive scoring, rating, and customer verification, as well as automated decision making to reduce underwriting cycle times and reduce human intervention. This frees up employees to focus on high-complexity underwriting cases.
- Reporting Data Aggregation Framework for HMDA: Multi-source data aggregation to generate a loan application register file and automate submission to the FFIEC. This can be automated as needed to handle the exception flow as appropriate for LAR submissions.
- Reporting Data Aggregation Framework for SCRA: Automates lookups for service members on active duty to ensure remedial actions are completed in compliance with SCRA regulations and extending protection to active service members. This lowers financial exposure due to reducing penalties and fines through automation
- Digital Intuitive Virtual Assistant (DIVA): Digital solution for call center transformation providing real-time guidance on next best actions, member profiling, next-call predictions, and text mining.
- Mailroom Transformation: Cognitive solution for email as well as paper-based mail validation, classification, routing and auto-response based on NLP and sensitivity analysis.
EXL’s BluePRINT™ Approach
EXL’s unique design thinking led approach is based upon digital discovery governed by generating actionable insights from information assets and data about customers, processes, and technology environments. This information is used to determine the right analytics and digital interventions for bringing the best business outcomes. The initial assessment can then be followed up with rapid prototyping and execution of sizable chunks of automation projects to get quick wins and build confidence.
Written by Kuldeep Martolia, Sundar Singh Bandi and Kalpesh Master