Revolutionizing automotive finance

The automotive finance industry is going through a dramatic shift, driven by technological advancements in vehicles, changing customer mobility and transportation preferences, and new industry players. Customers now expect flexible, highly personalized financing solutions, seamless omnichannel experiences, and agile customer service. To meet these needs, automotive finance companies must transition from traditional automotive lending entities to comprehensive mobility providers offering a full range of transportation solutions, including flexible financing, subscription services, and integrated mobility platforms. How can they achieve this? Digital transformation.

Many automotive finance companies have been held back by legacy practices, such as isolated process improvements that fail to consider the entire customer journey, disjointed data ecosystems, and a lack of investment in large-scale technology like contract management systems. To navigate the dynamic future of mobility, automotive finance companies must adopt a systematic and strategic approach. In our experience, adopting a building block approach that encompasses process, data, and technology, including artificial intelligence (AI), can make a significant difference (figure 1). This approach helps prepare automotive finance companies with proactive and hyper-personalized mobility offerings and intelligent services that provide a 360-degree view of both customers and vehicles.

To begin your transformation journey, establish customer-centric processes as the foundation. This requires companies to align processes with customers' needs, capturing customer data at critical touchpoints and enhancing the dealer experience.

Genpact Germany's 2024 customer experience study reveals that 56% of customers are not fully satisfied with their experience with automotive finance companies, despite 61% sharing personal data for tailored products and services. Value-stream mapping can address this contradiction. By placing the customer at the center, companies can redesign processes that capture interaction data across key stages – from sales and vehicle remarketing to customer retention – to personalize and enhance their experiences.

Redesigning processes to be customer-centric lays the groundwork for the building blocks that will prepare auto finance for the future: data, technology, and AI.

Key takeaway: Redesign processes with the customer at the center to capture valuable data and enhance experiences across all touchpoints.

Automotive finance companies can accelerate their evolution by strengthening how they identify, democratize, and build self-service capabilities for data. A future-ready automotive finance company must focus on proactively identifying customer demands rather than limiting itself to serving only basic requirements (Genpact Germany's customer experience study). Achieving this goal requires a strong data culture across the enterprise and at all levels of interaction.

Before scaling a data strategy, automotive finance companies must conduct a data assessment – a comprehensive evaluation of their existing data assets and capabilities. This assessment helps identify gaps, opportunities, and priorities for data management. To begin, involve functional teams – such as sales, customer service operations, risk vehicle management, and others – in this first step to identify the different types and formats of data within your systems. This will reveal detailed insights on customers, dealers, and third parties that companies have access to.

Companies should then work to integrate siloed datasets using data lakehouse principles (a hybrid approach combining the flexibility of data lakes with the structured data management of data warehouses) and make the data available to the broader organization. To make cross-functional data-driven insights more accessible, introduce self-service visualization dashboards that improve decision-making, data-driven operations, and operational fluidity across the enterprise, from product design and sales to customer service and asset management.

In a nutshell, creating a solid data platform integrates an automotive finance company's data and democratizes access to customer, dealer, and business insights. This paves the way for a data-driven future for mobility and secures a customer's or vehicle's lifetime value.

Key takeaway: Democratize data access across the organization to enable proactive, personalized mobility solutions and maximize customer lifetime value.

Modernizing automotive finance companies' enterprise technology is the second building block. Many automotive finance providers still rely on legacy systems for managing vehicle leases, loan originations, and fleet inventory. However, these outdated platforms struggle to support emerging mobility models such as subscription services, pay-per-use, and shared mobility solutions.

To address this challenge, companies should transition to cloud-native, API-enabled modular architectures. This approach creates the flexibility needed to offer innovative mobility financing products and seamless customer experiences. Here's a roadmap for modernization:

1. Expose legacy system capabilities through APIs
Create function-specific APIs for core automotive finance operations. For example, expose vehicle valuation algorithms, credit decisioning engines, payment processing modules, contract modification, and invoice generation capabilities as APIs from respective legacy systems

2. Restructure into microservices
Break down monolithic systems into smaller, more manageable components called microservices. This involves breaking down complex functions into simpler, standalone components that can be used across different applications. For instance, separate the vehicle lease management functionality into microservices for quotation, contract generation, and early termination calculations. Similarly, separate the contract modification capability into its own microservice, integrating it with relevant datasets. Restructuring into microservices enables companies to serve customers and dealers efficiently

3. Build a modular architecture
Group related microservices into reusable modules. This means organizing related microservices into larger, reusable modules that can be easily combined to create new services or applications. For example, combine vehicle valuation, residual value forecasting, and remarketing services into a vehicle life cycle management module. Likewise, combine contract modification, invoice generation, and customer data microservices into a contract management module

This phased approach allows automotive finance companies to gradually modernize their technology stacks while maintaining operational continuity. The resulting flexible architecture supports the rapid development of new mobility financing products, such as usage-based leasing models, integrated multimodal mobility subscriptions, and dynamic pricing for car-sharing fleets.

Key takeaway: Implement a phased approach to modernization, gradually building a flexible, API-enabled architecture that supports innovative mobility financing products.

The linchpin in the transition to becoming a future mobility provider is the considered and swift adoption of AI. With AI embedded across the enterprise, automotive finance companies can harness the vast volumes of vehicle and customer data while unlocking entirely new capabilities with generative AI (gen AI). However, introducing AI across an organization is not without its challenges, which include isolated AI initiatives, misalignment of AI use case selection with emerging mobility strategies, and difficulties in scaling AI models for diverse product offerings.

We recommend taking a step-by-step tailored approach to scaling AI:

1. Identify and prioritize AI use cases
Select use cases that align with future mobility trends, auto finance business goals, and technological feasibility. Focus on creating end-to-end value across the vehicle life cycle, not just short-term cost savings. For example, develop AI-powered residual value prediction models that can improve leasing terms and enhance portfolio management

2. Build a reproducible AI/machine learning operations (MLOps) architecture
Assemble cross-functional teams with expertise in both auto finance products and AI technologies. Implement MLOps platforms to accelerate the transition from credit-scoring proofs of concept to production-ready models for various mobility financing products

3. Use commercial generative AI models for enterprise-wide adoption in auto finance
Partner with providers of commercial gen-AI foundational models such as OpenAI's GPT models, Anthropic's Claude, or Meta's Llama to build applications specific to mobility financing. Establish a center of excellence to drive consistency in AI implementation across auto loan origination, lease management, and customer service operations

Key takeaway: Partner with gen-AI providers to customize foundational models for automotive finance, enabling enterprise-wide AI adoption and enhanced customer service.

As consumers shift toward new mobility options, automotive finance businesses must evolve to meet changing demands. Adopting an integrated building block approach empowers companies to start their transformation through incremental steps within existing constraints. This approach avoids common pitfalls like hyper-process optimization that does not consider the holistic customer experience, waiting for and investing in large-scale technology replacements, focusing on AI in isolation, or persisting with pilots without scaling the technology across the organization.

With a systematic approach in place, automotive finance companies can transition from traditional lending models toward becoming comprehensive mobility providers.

Authors

Alwin Bathija, Global Head of Advisory for Banking & Capital Markets, Genpact

Chaitanya Reddy Manchala, Senior Consultant, Financial Services, Genpact