Credit, Collections & Insolvency News
Artificial intelligence (AI), machine learning and robotic process automation (RPA) are key concepts and strategic elements of the future of credit and risk management, enabling us to make smarter analyses, make risk-free decisions and acquire more new, quality business. And automating decisions can do wonders today: algorithms maximise profit margins while filtering out fraud and saving valuable time for employees.
Optimised risk management
Up to 1-5% increase in sales volume. 80% of time savings. Make decisions ten or even a hundred times faster. If you think these numbers are far fetched – you are not alone with your opinion. Antti Merilehto, CEO of AI Strategy Company, believes many companies are completely unaware of the huge benefits already available today through AI-based data analysis.
“When I worked at Google, I experienced first-hand how effective automation is for analysing large amounts of data and how incredibly it can improve the efficiency and profitability of companies,” says Antti. “The harsh reality is that companies need to move to AI-based solutions here and now if they want to survive, and even those who are already cutting in can reap significant benefits. This has long been not just a thing of the future, and it is definitely not just a temporary phenomenon either. It is not enough for the CEO to tell the data controller to ‘start something with this thing called artificial intelligence.’ If the CEO is unaware of AI and machine learning opportunities and does not initiate change, the company can easily lag behind the competition. The use of AI must be one of the main priorities of corporate strategy; otherwise, it will never happen.”
“One of my favourite management excuses is, ‘If we get data of the right quality, then we’ll switch to AI-based solutions.’ It makes about as much sense as saying that I’m going to quit beer and pizza after I’ve already lost 12 pounds. In other words, they are waiting for something to magically happen before they are willing to start working”, says Antti.
Emelie Hultqvist, senior product manager at Bisnode Decisioning, points out that lending decisions and risk assessments are ideal applications for AI and RPA. The lion’s share of the work, which currently requires huge human resources, can thus be entrusted to algorithms. Such decision-making systems and credit rating models have already helped many companies automate risk management processes. To get started, companies don’t have to have all the information in advance, as service providers can supplement company data with their own high-quality data, such as information on banking transactions. In addition, the customer can also contribute to data collection, for example, through the use of loyalty cards that can be used to track shopping habits. Together with traditional credit risk information, this type of data gives even better results and further optimises the process. With the aid of analytical expertise and AI-based systems, these service providers can offer a solution even when a company does not know what to do with a large amount of data available.
Lightning-fast decisions
It’s not uncommon that clients want to see results right away, which requires a manual credit check and risk assessment. In this case, the service provider has to make a quick decision, and it can fall between a rock and a hard place, as they also have to pay attention to the needs of the customer and the requirements of their own finance department. However, all a business needs to do for automated credit decisions is to enter a personal or corporate ID number, and approval (or rejection) is immediately completed. Processes that once took days – such as submitting signed credit applications with references and similar documents – can now be completed in the blink of an eye. It can also be viewed that the least popular task is thus performed by an “employee” who never takes a lunch break, does not require sleep, and does not go on vacation. The benefits are clear:
Instead of tedious, monotonous work, strategic and creative tasks come to the fore in all areas. Approving companies with stable and high indicators (scores, results) without negative signals and screening out debt-stricken customers with payment problems are lengthy procedures. As a result of their automation, the company can devote its time and human resources to cases where it is really needed, which can be a straight path to development. For example, the credit department may focus on automated data analysis methods that promote growth instead of traditional accounting, which is also an area where AI makes work more efficient. Organising, filtering, and reviewing information is costly if done by a data analyst. Machine analytics simplifies and automates this process, eliminating intermediate links, helping to accelerate another area of decision-making.
Increasing traffic
The term “risk” took on a new meaning with the appearance of AI. Or rather, what we used to see as a risk may now even become a business opportunity. In addition to saving time and money, the technology guarantees the approval of applications from risk-free customers (not even accidentally rejected due to forecasts), leading to significant revenue growth.
It is possible that based on the analyses, a customer or potential partner does not appear to be creditworthy. However, AI-based data analysis also uses new criteria to identify individuals or companies with whom you can safely do business, despite previous (incorrectly established) alerts. In many cases, the human factor plays an important role, but AI can examine data much more deeply, and the information obtained in this way can provide a solid basis for smarter decisions, reduce the number of misjudgments, and eliminate the number of errors caused by negligence. In addition, customers will be grateful for the short waiting time and well-targeted offers.
Fraud screening
Unfortunately, invalid annual reports and fraudulent identity fraud are not uncommon. According to the Swedish Business Association, billions of Swedish krona are credited every year to fake companies, including those linked to organised crime.
Risk management means we are always one step ahead of fraudsters and the latest scam methods. For Erik Finné, risk manager at Ativo Finans, AI has become part of everyday work. The company uses a cognitive analysis system for text analysis and interpretation. Using another model, they are able to estimate the expected timing of the payment of bills and use a solution that identifies businesses with a high risk of bankruptcy that cannot be screened by traditional credit rating methods to ensure the reliability of their customers. The latter uses pattern recognition and network analysis based on data on past credit losses.
AI-based data analysis systems can predict if potential and existing customers are behaving suspiciously. Models developed using AI make it easier to identify new rules and patterns, even for large amounts of data from new sources. They can also be used to search for warning signs such as false annual reports and unusual patterns.
*Subramanya SN is a speaker at the Commercial Credit & Collections Conference. Join Subramanya on Thursday 17th June at 13:05, for a session discussing Technology and Credit Management.