36% of institutions are still in the early stages of financial crime technology adoption — fragmented, under-deployed, or not meaningfully in place at all. True end-to-end financial crime technology solutions remain rare. The market has historically been built around the needs of large institutions, leaving smaller firms to patch together point solutions, manual processes and tools that were never designed for their environment.

Market abuse is rarely visible from a single trade. Behaviours such as spoofing, insider dealing, marking the close and cross-product manipulation only become legible when orders, executions, cancellations, price movement, trader behaviour and venue activity are connected in the right way. That connection depends on data. And data is where most surveillance frameworks start to break down.

Communications supervision has become one of the most complex and consequential areas of market conduct and compliance. For decades, the focus was largely evidential. Firms captured email, voice, chat and messaging data, stored it in archives, and retrieved it when required by regulators, auditors or investigators. That model no longer reflects either the scale of communications risk or the expectations now being placed on firms.

Resilience has become one of the defining risk and compliance themes of the next market cycle. Firms are operating in a more complex risk environment, where technology dependencies, third-party exposure, operational resilience, regulatory obligations, internal controls and AI governance increasingly overlap.

Parker & Lawrence’s latest survey data suggests what many in the market already sense: financial crime is the risk domain where GenAI is producing the strongest financial upside. Financial crime is the top-ranked area of GenAI impact for almost a third of respondents, and half place it in their top three priorities for value creation. That reflects how quickly the economics of financial crime have outgrown legacy tooling.

Across studies, the same conclusion emerges: GenAI has collapsed the cost, effort and expertise required to create convincing fake documents, while dramatically increasing attack speed and volume. This has tipped the balance in favour of fraudsters, unless firms upgrade their detection capabilities. Identifying document fraud with AI is emerging as the most effective countermeasure.

The sheer volume of new regulations is increasing at an unprecedented rate, creating a density of obligations that manual processes cannot withstand. For compliance officers, the pressure is relentless: 64% of banking respondents in a recent risk survey cited "managing evolving regulatory change" as a high-priority concern, and 61% see reduced regulatory burdens as somewhat or very unlikely.

Generative AI is reshaping business models and risk profiles, creating opportunities for efficiency and competitiveness while heightening exposure to cybersecurity threats, fraud, model risk, and data governance failures. The Institute of Internal Auditors notes a “palpable” urgency among audit leaders to understand, govern, and adopt AI, even as regulatory clarity, controls, and internal capabilities lag.

As digital transformation accelerates, the systems that drive performance also amplify exposure. Organisations today depend on an expanding network of interconnected IT assets, cloud services, and AI applications. The scale of digital dependency is growing faster than most risk frameworks can keep up.

Physical risk across mining, infrastructure and heavy industry is escalating rapidly, ​​putting multiple stakeholders in jeopardy. For instance, Global landslides now cost $20 billion annually. In the U.S. alone, 2023 brought 28 separate billion-dollar weather disasters, totalling $93 billion in damages. Mining is 30–40% less digitally mature than comparable sectors like automotive, limiting its ability to modernise risk management. This blog introduces a new technology p