From Year-End Scramble to Real-Time Decision-Making: How to Automate ESG Impact Tracking

Reconstructing ESG (Environmental, Social, and Governance) balances at year-end is an exhausting and often imprecise task for financial institutions. Hatem Dhahri, CEO of Neovar and Partner at Talys Group, advocates for a fundamental shift in this approach. In this interview, he proposes inverting the logic by automating the collection of impact indicators from the very beginning of the credit assessment process, thereby transforming regulatory requirements into a genuine decision-making lever.

Can technology make finance truly responsible today?

Not automatically. For the past twenty years, we have been digitizing financial institutions, primarily to gain speed, reduce costs, and improve the customer experience. This digitalization was essential; however, it is not sufficient on its own to make financing more responsible. The real question is no longer how to finance faster, but rather understanding the nature of what we are financing, its impact, and how to measure it. Without the right tools, ESG remains largely declarative—displayed commitments, but data that is difficult to verify.

Concretely, how can ESG be integrated into the daily operations of a financial institution?

The most natural approach is to start where the critical decisions are made: at the point of financing decision. Consider a small and medium-sized enterprise (SME) applying for equipment credit. The bank already collects its financial statements, guarantees, and credit history. It can integrate a few impact criteria into this same workflow: Does this financing create jobs? Does the equipment help reduce energy consumption? Does it support an activity with positive social impact? These are data points that integrate naturally into the credit approval process. Technology then allows each institution to configure its own criteria based on its strategy, market, and regulatory framework, without burdening the relationship manager.

There is a lot of talk about ESG reporting. Is that not enough?

That is one of the pitfalls. Many institutions still approach ESG through the lens of reporting. At year-end, they must search for scattered information and reconstruct their history. In my view, this logic must be inverted: capture ESG data at the source, at the moment the financing is being studied and approved. Reporting then becomes a consequence of the process, rather than an a posteriori reconstruction. The bank can track its portfolio, the jobs supported, and its environmental indicators. This shifts the focus from reporting to decision-making. This is, in fact, what regulators and international lenders are beginning to demand: not just principled commitments, but traceable figures, financing by financing.

How does this evolution translate at Neovar?

Initially, our core business is credit, risk, and financial decision-making technology. Historically, our projects focused on the digitalization of workflows and risk management. Today, we also integrate data analytics, artificial intelligence, and early warning mechanisms, as well as the consideration of impact criteria in financing. However, the tool remains a means to an end. We do not claim to define banks’ ESG policies. Our role is to help them translate those policies into concrete, measurable criteria integrated into the daily credit process.

Responsible finance also involves financial inclusion. Does technology have a role to play?

A major role, particularly in our region. Digitalization reduces the cost of processing a file and can make it viable to serve segments that have historically been underserved, such as micro-enterprises or populations distant from traditional banking. However, inclusion does not simply mean lending more. It requires offering the right financing to a person or business capable of absorbing it, under sustainable conditions. Technology can precisely help analyze these situations better and reconcile inclusion with risk management.

What about AI in all of this?

AI already allows for the analysis of more data and the detection of weak signals. But in finance, a faster decision is not necessarily a better decision. A model that cannot be explained or governed is also a risk, particularly regarding algorithmic bias and data protection. The more we automate, the more important governance becomes. Responsibility must remain human. Technology is there to equip humans, not to replace them.

What will be the main change in the coming years?

The end of silos. For a long time, credit, risk, and CSR (Corporate Social Responsibility) lived in separate worlds. Data now allows these functions to converge. Tomorrow, institutions will no longer manage their portfolios on two dimensions—performance and risk—but on three: performance, risk, and impact. The day this third dimension is measured with the same rigor as the other two, responsible finance will cease to be a discourse and become a practice.