On Tuesday 3 February, Reuters (and others) reported a broad selloff in global software stocks that saw the S&P 500 software and services index fall by nearly 4%. The sector slipped another 0.73% on Wednesday, marking the sixth consecutive session of losses and a drop of about $830 billion in market value since 28 January.

The selloff is affecting the price of many players in the risk technology universe, including S&P Global, FactSet, LSEG and RELX. The reason: the market believes that the broader use of AI will result in an increase in DIY software and data sciences. The reduced sales growth and profitability of some software as a service (SaaS) vendors certainly lends credence to this thesis. But Chartis believes that the market’s view is too indiscriminate and too focused on AI. The real story is far more complicated.

Software implications

It’s true that vendors without complicated internal data models or complex analytics embedded in their software will face immense pressure – but not because institutions will be developing their own software. Rather, the choice of new vendors will be wider. This is partly due to the enabling influence of new ‘AI-driven coding’. But a bigger driver is the availability of open-source options, including versions of critical data infrastructure elements and, in the risk technology space, more widespread availability and understanding of risk software intermediates (such as math libraries and cashflow languages). However, the impact of these drivers will vary across the vendor landscape.

In its RiskTech100 2026^® report, Chartis made the case that while vendors such as Murex, Nasdaq Calypso and Numerix are immovable in their respective markets, many other vendors with lighter IP and less complex data infrastructures will see their markets and margins nibbled away by both new and established providers. No one is suggesting that companies such as ServiceNow will disappear – rather that cheaper, simpler versions of these firms will appear rapidly in the market (and indeed are already doing so).

Similarly, while SAP will continue to dominate, domain-specific vendors (such as ION in bulk energy and Quoreka in other metals) will prove difficult to shift, while companies such as Murex, Numerix and Moody’s will be incredibly resilient, because replicating their internal models is a gargantuan task that most institutions will be reluctant to attempt.

Data implications

While the barriers to entry in the data space are lower than ever before, the key factors in this market are the type of data being used, the complexity involved in collecting it, and the nature of any added value it may create. Among data providers, for example, both IP and complexity vary enormously, even within individual platforms. Moody’s, for instance, has collected vast amounts of non-public data that is extremely difficult to acquire. Moreover, Moody’s large corporate database is difficult to replicate, as is LexisNexis Risk Solutions’ approach to what Chartis terms ‘software as data’.

The current decline in stock prices highlights an interesting point: both the so-called ‘disruptors’, and the firms being disrupted, have lost value in different ways. The market has been equally challenging for firms that rely on high-performance/’sticky’ case management and workflow to move their customers and margins outside specific niches. While general-purpose workflows/business process management (BPM) providers may struggle, companies such as Fenergo, a specialist, will not.

In short, then, the more specialized the company, the more complex data and models it uses, and the more non-public data it owns, the better. PremiaLab, an example of a specialized firm operating in a highly defendable niche, recently raised capital in part due to the specificity of its business, as others will struggle to replicate its trust and history. Equally, offering a narrow and very targeted focus is another viable option.

Concluding thoughts

The reaction of the market has been to penalize everybody, but Chartis believes that some companies will emerge stronger as the market realizes just how robust their competitive ‘moats’ are. The market is also yet to appreciate the differences between the various business models in operation, including software as a service, data aggregation, managed services, or ‘software as data’.