Harnessing AI and digital innovation to drive the future of automotive industry
The arrival of GenAI has kick-started the rapid application of intelligent systems across the automotive industry. Finance firms, insurers and leasing companies in the industry are using GenAI to automate their interactions with customers, quicken their processing of key data and tighten their control of valuable assets. Many are also relooking at the potential of well-established, classical, AI systems to improve, among other things, fraud detection, customer retention and risk analysis.
“Classical AI, what we’ve been doing for a long time, has become more relevant now. People are paying more attention to its uses in applications like customer retention and payment predictions. Before ChatGPT came around there wasn’t such interest,” says Marc Koehne, Head of Business Model Development at BMW Financial Services.
Koehne oversees the data science team at the finance provider which is using classical AI and GenAI to develop intelligent systems that drive the organization’s growth.
“We have huge expectations for economic benefits of our work. For every euro we spend, we’re expected to get two, three or four back.”
Koehne was one of the key speakers at an online event, hosted by Qorus and CVA, that examined the impact of AI and digital innovation on the auto industry. Representatives from CGI Finance in France, Czech insurer Direct Pojišťovna and event co-host CVA also shared their insights into how AI is changing businesses in the automotive sector.
AI has huge potential to reduce costs and improve margins among downstream companies in the auto industry, says Markus Collet, Partner and Head of Automobility Platform at CVA.
“New and used car sales processes as well as fleet sales can all be improved using AI and in the financing value chain there are lots of use cases such as better pricing, underwriting and fraud detection.”
“With additional data, companies can better steer their processes. So, data management with AI becomes more important.”
Markus Collet, Partner, CVACollet points out that classical AI systems have typically been rules-based solutions that use structured data to perform narrowly-defined tasks. GenAI, in contrast, is able to generate output dynamically by using a variety of data forms and it can engage with operators using natural language.
AI is becoming increasingly powerful downstream in the auto industry because of the vast volumes of data that companies are generating as they digitize their businesses, the rise in their computing capabilities and the shift to more centralized management, says Collet.
“With additional data, companies can better steer their processes. So, data management with AI becomes more important.”
Auto industry executives are increasingly recognizing opportunities to use AI to improve their businesses. Almost all the leaders polled during the online event believe AI will have a considerable impact on the auto industry. Here’s how they ranked its likely effect on their companies:
- Critical - 40%.
- Significant - 30%.
- Decisive for survival - 26%.
- Marginal - 4%.
“AI has been a central part of our strategy for several years. It is not just another tool, it’s a strategic lever,”
Laurent Boudet, Director of Data and Digital Solutions and Innovation, CGI FinanceHowever, only a few of the executives believe their businesses are capitalizing on the full potential of AI. When asked to rank the extent of their AI usage on a scale of 1 to 10, only 24% gave their firms a rating of 6 or more. Most companies are still in the early stages of developing their AI applications.
“AI has been a central part of our strategy for several years. It is not just another tool, it’s a strategic lever,” - Laurent Boudet, CGI Finance.
CGI Finance, a subsidiary of financial services giant Société Générale, is one of the auto industry’s AI pioneers.
The company uses AI to support its dealers and partners, manage risk, and engage with customers.
Key applications include:
- Early detection of business opportunities for dealers: AI predictive models within the company’s VivaBI platform detect early signals of lease termination, identify leads, and trigger renewal offers that strengthen customer loyalty.
- Residual value and risk management: AI systems improve asset performance by dynamically assessing vehicle residual values; managing return rate, repayment and early payment risks; and developing personalized offers with remarketing partners.
- Credit risk and customer experience enhancements: Advanced customer scoring using AI improves the accuracy of credit assessments and streamlines approvals; reduces service costs; and enhances the customer experience by reducing turnaround times.
“AI is a true performance driver for us. It also helps us uncover opportunities for our customers,” adds Boudet.
“We can scale up the process infinitely because the automated claims manager works 24/7.”
Michal Kozub, Digitalization Leader, Direct PojišťovnaCzech insurer Direct Pojišťovna has dramatically improved its efficiency by using AI to automate its claims handling.
“Originally, the process of investigating a damaged windscreen claim took about 20 minutes because the agent had to open documents, check pictures, and determine what had happened. Now, with AI the process lasts about 53 seconds,” says Michal Kozub, Digitalization Leader at Direct Pojišťovna.
AI automation has cut claims handling FTE requirements by 60%, adds Kozub.
“We can scale up the process infinitely because the automated claims manager works 24/7. Next, we’re looking at CASCO (Comprehensive Auto Insurance) and MTPL (Motor Third-Party Liability) claims management and later on property and travel insurance claims processing.”
The ability to scale AI solutions is critical when selecting applications to automate, says Koehne at BMW Financial Services.
“We are dedicating about 80% of our capacity to developing standard applications that have a high capacity to scale. The remainder is for applications where we don’t know their potential yet.”
To improve the effectiveness of its AI development, BMW Financial Services has created a production framework. The company encourages employees to use an in-house chatbot service to submit suggestions that would improve the business. These suggestions are reviewed by an AI council, which comprises representatives from across the company, and assessed to determine their likely profitability, requirements, risk and compliance. Those that are selected are forwarded to be developed by the central AI engineering team.
Automated text processing, customer behavior indicators and automated speech analysis are the AI applications most in demand within the company, says Koehne.
Speakers at the online event pointed to three major hurdles that often impede the roll-out of AI applications:
1. Integration with legacy IT systems.
2. Resistance to change among senior management.
3. Compliance and regulatory constraints.
Considerable preparation, planning and participation of key leaders is required to overcome these obstacles.
“There are a lot of best practices that companies in the US and Europe can learn from China.”
Xin Gao, Associate Partner, CVAXin Gao, Associate Partner at CVA, points out that collaboration among a wide range of participants has enabled financial services companies in auto industry in China to race to the forefront of AI innovation.
“Financial institutions in China want to embrace big AI models. But they realize it’s difficult to develop them by themselves. So, there’s a consensus among leading players in China. They are deploying big AI models and training them for localization.”
Finance providers in the Chinese auto industry have introduced several advanced AI solutions. Cherry FS, for example, has developed a digital human-face reviewer that uses AI to evaluate customer risk, adapt review processes in real time, and guide clients as they apply for finance. Yixin is using the DeepSeek GenAI solution to improve the performance of its in-house operations.
“There are a lot of best practices that companies in the US and Europe can learn from China,” adds Gao.
As downstream operators in the auto industry extend and improve their AI applications intelligent automation will become an essential component of their risk management, customer service and the deployment of assets. The most effective AI applications will be those that are aligned with the strategic goals of the business, are supported by all levels of the organizations, and draw on the capabilities of key business and technology partners.
