As a technological leader in debt collection, PAIR Finance has been advancing artificial intelligence (AI) development for years while tracking its overall technological progress. In our series « Spot on AI, » we continuously report on artificial intelligence from a fintech perspective, provide insights into our own technology at PAIR Finance, and share our assessments of numerous new developments.
Today, we turn our attention to the technology of the moment which is transforming everything in finance: generative AI. For businesses, it represents significant opportunities for cost savings and increased revenue, while also presenting major tasks to remain competitive in the long term.
We reveal what consumers expect from companies, how firms aim to use generative AI in the financial sector, and what the biggest challenges for businesses currently are. Additionally, you’ll learn how PAIR Finance applies generative AI in debt collection to make claims management even more efficient and customer-focused.
Generative AI is the key technology of our time
Generative Artificial Intelligence (also GenAI) is a technology that uses deep learning and neural networks to create new, original content such as text, images, code, or videos. Unlike other types of AI, which focus on classifying or identifying content, generative AI can generate entirely new material. Well-known tools leveraging this new technology include large language models (LLMs) like ChatGPT, Gemini, Llama 3, DALL-E, or Claude. Its cross-industry disruptive potential and accessibility make generative AI the key technology of our time.
LLMs automate all sorts of tasks and can be used by anyone without requiring an understanding of the underlying technology. This accessibility is one reason for the massive spread of generative AI.
The development of AI is as fundamental as the creation of the microprocessor, the personal computer, the Internet, and the mobile phone. It will change the way people work, learn, travel, get health care, and communicate with each other.
Bill Gates
Generative AI: A Gamechanger for the Financial Sector
The success of technologies like ChatGPT has brought generative artificial intelligence (GenAI) into the spotlight of the financial world. When implemented effectively, this technology can not only revolutionise the operations and business models of financial firms, but also elevate the customer experience to an entirely new level. According to a recent PwC study, two-thirds of financial service providers are already using AI technologies – and the majority plan to significantly expand their usage in the coming years.
However, despite its enormous potential, only 10% of companies currently deploy generative AI on a large scale. The main reason: many struggle to identify the best use cases. In addition, concerns about legal and regulatory requirements, as well as a lack of qualified specialists, hold many back. Still, according to Forbes, 85% of financial sector executives plan to significantly increase their investments in GenAI.
Generative AI solutions in finance, according to IBM, focus on advanced AI applications to automate processes, enhance customer service, and detect fraud. Through machine learning, businesses can quickly and accurately respond to customer inquiries and provide personalised financial advice. Moreover, generative AI is used to identify suspicious behaviour early on, boosting security. It also improves administrative efficiency by simplifying tasks and shortening processing times.
How PAIR Finance successfully uses generative AI in debt collection
Since its founding, PAIR Finance has consistently relied on the automation of dunning processes with AI to optimise debt management and create the most user-friendly debt collection experience for consumers. As early as 2018, we began using reinforcement learning to address customer types individually and develop out-of-court solutions for outstanding invoices.
Through tailored typologies and personalised communication developed by our team of behavioural researchers, we motivate consumers to proactively address their outstanding debts. This approach reduces costs while simultaneously increasing recovery rates.
With our generative AI technology specifically developed for debt collection, we have elevated customer service communication to a new level. The AI categorises over 92% of incoming first-level inquiries, such as instalment requests, payment pauses, or disputes. Based on the inquiries, the technology determines whether an automated response is possible. For more complex issues or cases requiring specialised expertise, consumers are referred to qualified employees who provide tailored solutions.
The impressive results of our AI-based debt collection solution can be found in our press release on the launch of generative AI technology. Overall, PAIR Finance has now resolved 90% of successfully completed cases with the help of reinforcement learning and generative AI technologies.
From Cost Saver to potential revenue driver
The economy holds high expectations for modern technology, particularly generative AI. Three out of four managers worldwide believe generative AI will benefit their business (Capgemini). Moreover, nearly all surveyed executives (96% globally and 99% in Germany) see it as a key technology for company leadership.
These managers expect that successfully integrating generative AI into IT infrastructures can lead to an 8% increase in revenue and a 7% reduction in costs over the next three years. According to a representative Civey survey commissioned by Microsoft, German companies cite cost reduction, increased efficiency, and improved business processes as the three main advantages of generative AI.
The goals businesses pursue with generative AI also depend on whether they are already actively using it or are still in the planning stages. The 2023 State of AI Report by McKinsey shows that companies heavily using AI today focus less on cost savings than others. High-performing AI users are twice as likely than others to use AI to explore new business areas or revenue streams. Find out from our CTO Dmitry Sharkov and our Team Lead Data Science Maxime Kaniewicz how PAIR Finance is using AI to revolutionise the debt collection experience.
Overall, the trend is for companies to no longer see artificial intelligence as a mere opportunity to save costs, but as a revenue driver. In a S&P survey published in August 2023, 69% of the more than 1,500 respondents cited revenue-related factors as the motivation for AI projects in their companies. The survey results also show that the more companies are already working with AI, the more they are focusing on AI’s potential to increase revenue.
Data management, compliance, and ethical concerns are the biggest challenges in applying generative AI in the financial sector
According to the S&P survey, data management is currently the most frequently cited obstacle for companies looking to adopt artificial intelligence, with 32% identifying it as a challenge. This is followed by security challenges (26%) and computational capacity (20%). Company data is often scattered across various formats and locations, lacking proper labelling or cataloguing. Consequently, many companies are not yet ready to utilise AI effectively.
In the financial industry, compliance with regulatory standards is a central challenge. The sector operates under strict legal requirements, such as the GDPR and Germany’s Federal Data Protection Act (BDSG), which regulate the handling and storage of sensitive customer data. Generative AI must ensure that all data is encrypted and GDPR-compliant to avoid hefty fines or reputational damage.
Additionally, ethical concerns raise questions about the fairness and transparency of AI algorithms. Companies must ensure that generative AI does not produce discriminatory results, particularly when it influences credit decisions or risk assessments. Another ethical dilemma lies in balancing automation with human oversight: how much responsibility should be delegated to AI in sensitive financial decisions?
To overcome these challenges, financial sector companies need to improve their data structures and security protocols while developing transparent, ethical, and discrimination-free AI models that meet the industry’s high standards. Only then can generative AI reach its full potential without eroding trust.
Conclusion: Generative AI Shapes the Future of the Financial Industry
Generative AI has the potential to fundamentally transform the financial sector. When implemented correctly, companies can streamline processes, enhance the customer experience, and achieve both cost reductions and revenue growth.
Automation through generative AI not only relieves companies but also provides consumers with innovative financial service solutions.
However, the path to fully integrating generative AI remains challenging. Issues such as inadequate data management, complex compliance requirements, and ethical considerations must be addressed. Particularly in the highly regulated financial sector, it is essential to develop transparent, GDPR-compliant, and discrimination-free AI models that build trust and meet the highest standards.
The financial world stands at a turning point: generative AI is more than a tool – it is a transformative force that not only optimises costs but also enables new business models and creates competitive advantages. Companies that use this technology purposefully and responsibly are actively shaping the future of the industry and setting benchmarks for innovation and customer focus.
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