A motorist who received a fine after driving through Gatwick Airport's drop-off area challenged it using ChatGPT artificial intelligence (AI) and won a much-reduced penalty. Shaun Bosley, from Brighton, was dropping a work colleague at the airport last November and received a £100 "final notice" from NCP several months later, despite saying he had received no previous correspondence. Mr Bosley, a sales consultant for Phyron, a Swedish company which produces videos for car dealerships using AI, turned to ChatGPT, which generates human-like conversations. Users simply type a request into a chat box and the system can generate a response almost instantly. I didn't have to look at it and think'that sounds like a robot, I need to change some of it' "In the end, I just typed, 'write an appeal to a penalty charge notice for driving through Gatwick airport. I have received final notice, but never received first notice of the penalty', and straight away it came back with a great response," he told the PA news agency.

One True Holding Company d/b/a/ TrueML, a financial technology software company developing machine learning-driven products that revolutionize the debt collection process, today announced that Steve Carlson will join the company as president. He joins as TrueML readies to accelerate its growth, with plans to expand its industry leading software solution, doubling run rate to reach more consumers with a digital first experience. TrueML develops software including patented machine learning technology to create a digital-first debt collection process that aligns with consumer communication preferences. Formerly One True Holding Company, the financial technology company rebranded in July 2022 as TrueML to bring more focus to its machine learning software development, which is a key aspect of its overall growth strategy. As a mission-driven company, TrueML aims to bring solutions to the marketplace that redefine how creditors and consumers engage in debt collection.

Developments in machine learning (ML) and Artificial Intelligence (AI) are having a great impact on the debt collection industry. At its core Machine Learning generates predictive models using algorithms that learn from data. The idea is that if we can input enough useful and reliable data, we can build models which can make predictions on our behalf. There are a number of ways in which machine learning can aid and improve the debt collection process: Reduce Workloads Collections departments place calls, send countless emails, and seek to work out payment plans — and very frequently none of the above activities translate into the successful recovery of debt. With ML this changes. Since tasks are automated, users experience higher productivity and less time spent on labour-intensive tasks. Protecting Your Business Reputation Since ML can automate communication, you know that all your business correspondence will be professional, methodical and unambiguous. LATERAL’S debt collections software provides its users with a non-intrusive, customer-driven point of engagement, which is proven to be highly successful.  

In addition to conventional decision logics for determining the next step, there are many other exciting applications in receivables management, including the automation of complex written communication with end customers and third parties. This requires the accurate recognition of incoming queries and the correct deduction of next measures. Performing this assessment using artificial intelligence enables the direct initiation of steps, respectively context-dependent forwarding to the right administrators. By combining machine learning with human experience, this allows queries to be answered effectively and as quickly as possible. After deciding to entrust manual processes in debt collection to artificial intelligence, two important topics need to be considered: firstly, the quantity, nature and quality of the database that can be used for modelling.