Managing model risk and adhering to institutional compliance expectations at scale requires extensive effort and exceptional attention to detail. However, even the most diligent risk managers are frequently restricted by continuous changes to regulations and market demands, and limited by manual and outdated process controls.
Acting as a process augmentation, the right automation solution can support broader visibility and faster decision-making, leading to more reliable financial risk analysis and regulatory reporting. Long-term, it can also support ongoing model monitoring and governance activities, moving managers away from periodic reviews.
KPMG reports that 71% of organizations using AI in financial planning find that it is either meeting or delivering beyond ROI expectations. Beyond surveys, however, it is important to understand how automation aims to make risk handling more dynamic.
Gurpreet Chaggar of Prophix
Proactive financial risk management allows risk managers to transition from historical analyses to rolling, continuous vigilance. This shift is increasingly supported by AI tools for finance that automate data aggregation and continuous monitoring across complex institutional environments.
For example, real-time data and analysis allow teams to build and stress-test adverse economic scenarios more quickly, as conditions evolve, and as resource availability changes.
Conversely, relying on reactive, manual risk management processes results in slower decision-making and post-crisis response. What’s more, leadership risks relying on outdated information, driving down the quality of financial models and the decisions they lead to.
Issues relating to visibility, control, compliance, data quality, and cycle times can impact financial risk assessments and their reliability.
Data visibility and control: Without a clear, single source of truth for financial data, finance teams cannot accurately measure risks in real time. Data dispersed across systems and silos that don’t integrate leads to finance teams having to prepare for risk “in the dark”. That is, unless they take extensive manual steps to ensure information reconciles.
Evolving compliance: As compliance demands scale and become more complex, static or manual finance processes struggle to keep up. It is increasingly challenging for institutions to comply with changes to regulatory reporting requirements and operational standards, meaning risk managers frequently need to keep abreast in real time.
BCBS 239 risk data reporting expectations, for example, require institutions to ensure data is granular and transparent, record clear journeys, and apply clear, consistent data ownership. Yet, as recently as 2024, only two of 31 global systemically important banks (G-SIBs) were deemed as fully compliant.
Data quality: Poor-quality data is incomplete, inconsistent, and difficult to understand (whether through version control confusion or otherwise). Without centralized, consistent data flows, risk managers face building unreliable and unrealistic models and scenarios, leading to unreliable stress tests.
Cycle times: Given the evolving nature of global financial markets and compliance needs, risk managers are under pressure to develop models, test scenarios, and build reports at speed. Challenges with data access, control, and quality, however, extend scenario-building and testing cycles, meaning reports become outdated, unreliable, and potentially non-compliant.
Banks and institutions with inefficient and ineffective risk management strategies face severe financial loss, systemic instabilities that affect firms across the broader system, and compounding reputational damage with stakeholders, account owners, and regulators.
Multiple high-profile collapses over the years, within the 2008 crisis and outside it, demonstrate the clear cost of even minor lapses in risk oversight.
Silicon Valley Bank suffered a complete collapse despite attestations of robust risk management policies. Issues such as poor board oversight and an inability to anticipate liquidity risks led to the institution’s demise in 2023, when it faced a deposit run of $42 billion in a single day.
Automation strengthens three-lines-of-defense (3LOD) models by enhancing data transparency, traceability, and control ownership. Managers have greater oversight and can validate models faster, and there is detailed logging of all decisions made, ready for the audit.
Delegating high-volume tasks to finance automation accelerates data capture and provides risk managers with more accurate, timely information. This rolling visibility helps them proactively mitigate risks, build more reliable, up-to-date reports, and align more strongly with complex expectations such as BCBS 239.
This is supported further by finance automation detecting anomalies and raising exceptions for human attention as they emerge. Instead of reliance on manually investigating and reconciling numbers at scale, automation delivers alerts as they arise. Risk managers no longer need to wait for periodic reports, therefore gaining up-to-date insight into factors such as liquidity.
Automating data collection and aggregation gives risk managers and teams more time to focus on high-value analysis. Crucially, reliable automation allows risk and governance teams to keep control of thresholds and escalation rules, and supports the building of approval workflows and structures. They are flexible, too, should stakeholder and compliance needs change.
Implementing finance automation should be a careful, phased process, allowing managers to adjust what AI accesses and the decisions it makes. While best practices will look slightly different for each institution, there are general guidelines that help to make adoption efficient, controllable, and productive:
Effective risk management relies on absolute data visibility, quality, and control. Otherwise, managers build and stress-test scenarios based on assumptions, not finite information. In response to such challenges, across broader financial management, nearly two-thirds of CFOs report that their firms intend to prioritize task automation in the short term.
When implemented effectively and controlled by human guardrails, automation supports a clearer, more accurate depiction of institutional finance health – augmenting, not replacing, human expertise.
Gurpreet Chaggar is an associate product marketing manager at Prophix. She joined the finance platform company in 2019 as an implementation consultant, where she developed a deep understanding of Prophix's solutions and the impact Prophix has on helping clients optimize business outcomes.