TechnologyAI delivers a paradigm shift for credit management

AI delivers a paradigm shift for credit management

Embracing and implementing AI-based technologies can make functions such as credit management more efficient and cost-effective, while minimizing risk along the way, writes Sébastien Méric, chief innovation officer at Tinubu Square.

“We’ve always done it this way…” A common enough refrain in many industries, but if we allowed it to set the pace, no company would ever get out of its comfort zone, never evolve, and certainly never improve.

This is certainly true when it comes to the credit management function. For a start, definitions of credit management can portray it as a cost centre, with no role to play in generating profit and no intrinsic added value. In today’s economic climate, credit management is vital, it’s an activity inherent to inter-company financing and our own professionals have to recognize and promote it as such.

Part of this is about seeing credit management through a new lens, and recognizing the part it has to play in delivering an indispensable service. It is also about accepting that innovation can help our industry immeasurably and, instead of sticking with entrenched views and practices, embracing technology that can propel us into the future.

We’re not looking to add more horses to the carriage, we want to get on board with the internal combustion engine.

Artificial intelligence: oil on all the gears

Artificial intelligence (AI) is just one example of innovation that has a massive part to play. Put simply AI involves computer programs that are able to automatically generate cognitive functions. In practical applications, it specifically involves robots that can adapt to situations not initially predicted by the engineers who designed them. Therefore, a robot must be able to adapt its behavior within situations in which it has never before operated, relying on experience or knowledge from past experiences.

Without AI, when an automatic process encounters an ’anomaly’, it waits for human intervention. For example, reformatting a file to be used in a database. With trained AI, the robot will know how to adapt, going as far as collecting data directly from an e-mail, in files structured (differently each time) by a company’s clients or in a PDF, and even in a chat on Messenger.

Today, algorithms are able to track information about a sector, a company or a behavior directly online. When properly designed and/or trained, algorithms can identify general trends of perception by internet users on a given theme.

Machines can find connections that our human brains, clouded by bias, cannot detect.

Machines are able to produce acute analyses, following a number of important criteria. Compared to the human brain, they have a superior ability to extract trends from data flow. While human beings can track close to seven indicators, machines can effortlessly follow several hundred and are faster to uncover fraud risk or an underlying trend in a business sector. Machines can find connections that our human brains, clouded by bias, cannot detect.

AI allows credit managers to automate processes all the way up to the most important decisions, such as establishing a credit limit. AI is able to reproduce the reasons for its decision in human language, and this decision itself is auditable, even though algorithms are not entirely deterministic at least not in the sense of our ability to perceive their reasoning).

From data plants to financial information mills

Enterprises are rarely able to evaluate risk alone, and can never insure themselves against this risk, which is why they turn to a third party: an insurer who, having evaluation tools and guaranteed capital, can guide them.

In the same way that credit risk management is often perceived as a constraint on business, an anti-growth burden, so credit insurers are regarded as institutions with slow, frustrating administrative processes, amassing piles of paperwork (even if it’s digital). This can be especially difficult when it comes to managing a claim.

To reiterate, credit risk is a simple consequence of inter-company financing. It’s one of the main financial driving forces of the value chain in which a company is situated, a form of mutual funding. To make this driving force operate, credit managers have to be able to evaluate their clients, to offer them financing through credit tailored to them.

Collecting data in a silo has allowed insurers in general, and credit insurers in particular, to stand out from competitors for decades. By studying this data, they are able to both refine their risk perception and offer prices and products that are tailored to their clients.

Since the establishment of the GDPR (General Data Protection Regulation), the model of data being the property of those who collect it, has shifted. Data now belongs to those who produce it, at the very least in the case of personal data, and an extension to other types of data is being studied by the European Commission. This model is based on the idea that our digital self is an extension of our physical self. This can easily be applied to a corporate body. In this case, a company’s financial data belongs to the company (even if the law requires them to make it public), and methods of accessing this information will now undergo changes.

Just like information made available online, open data movements—paired with shifts on data ownership and the dawn of deep learning after a decade-long slumber—will change the credit insurance profession.

Using this combination of data freedom and AI allows credit managers to create consulting and guiding services

Access to this data has been transformed and simplified. Less and less expertise is required to use this data; knowledge and its application is increasingly found in intelligent robots. Using this combination of data freedom and AI allows credit managers to create consulting and guiding services. To stand out, we must invent new services for clients. Therefore, this involves breaking the habit of seeing data as a company’s own property, and envisioning sharing it with competitors, in order to:

  • Have an expanded data panel to build new models
  • Refocus company efforts on value creation rather than protecting data property

By sharing company profile data rather than keeping it in lockdown, an insurer can then focus on steering the value chain, monitoring its health, offering solutions in difficult situations, and optimizing inter-company financing in this chain to improve its performance.

Finally, insurers and credit managers can focus on the value that they can bring to the market and be seen to be doing so. Data flow is the lifeblood of commerce. Insurers must capture this to produce the driving force for company financing. Managing credit risk is not limited to ’scoring’ a company and granting it a credit limit. It also means being able to play with the credit periods, cancelation strategies, transactions themselves and their criticality. An Augmented Risk Analyst (ARA) can monitor and guide all these variables easily and effectively.

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