Transforming Risk Management in Financial Services with Generative AI - SPONSOR CONTENT FROM PROVECTUS AND AWS

Sponsor content from Provectus and AWS.

By Helen Johnson In financial services, risk exposures are shifting faster than ever. Credit risk can change in days, fraud patterns evolve overnight, and new regulations arrive with little warning. Yet risk management teams are still expected to work with processes and tools designed for a more leisurely age—manual reviews of hundred-page documents, hand-typed case narratives and memos, and compliance processes that chase regulations one change at a time. Generative AI (gen AI) offers a different path, changing how risk teams work. Instead of spending hours manually combing through documents, teams can receive clear, decision-ready summaries in minutes. While technology does not replace professional judgment, it accelerates the groundwork, allowing experts to concentrate on assessing exposures and taking action before risks escalate. Workflows Designed for Yesterday Financial organizations run on data, but most of it is locked in documents and is not analysis-ready. Transaction histories, loan documents, suspicious activity report (SAR) narratives, policy manuals, and claims notes are scattered across formats and systems. Risk professionals across almost any workflow must process, repackage, and interpret these documents before they can make a decision. This creates three points of friction: • Cycle times are considerably extended as risk managers and investigators wait for summaries and reports. • Different experts across the workflow highlight different facts, leading to inconsistency in risk assessments and approvals. • Fraud indicators are hidden in a diverse set of documents and data, with no unified view of exposure. The outcome is clear: slower growth, higher costs, and missed risks. When Risk Teams Fall Behind The consequences of inefficient risk management are immediate. Delayed analysis, insight, response, and approvals reduce win rates. Alert fatigue and inconsistent investigations can let fraud slip through the cracks while valid customers face friction. Compliance costs escalate as staff manually track regulatory change. Model documentation lags behind actual model use, creating governance gaps. Strategically, the impact runs even deeper. Fragmented risk views lead to mispriced products and reactive capital planning. Expertise becomes a major bottleneck, slowing entry into new markets. And regulators who demand explainability, data lineage, and fairness do not accept excuses about human bandwidth. Risk teams need AI to keep up, but AI itself can create new vulnerabilities: hallucinated “facts,” biased outputs, privacy leakage, and unclear reasoning. The challenge for finance leaders is to reinvent their business model and modernize operations without losing control. An Operating Model Shift Financial services businesses should regard gen AI not as a plug-in technology but as a strategic shift in how they manage risk work. Organizations that succeed approach it with five guiding principles: • Make data usable. Build governed pipelines for documents and other sources of data. Extract, process, and classify content so gen AI can retrieve and summarize facts with traceable citations. Capture human edits to improve quality over time. • Establish a gen AI platform for innovation. Successful gen AI adoption is not about running isolated pilots. Amazon Bedrock provides a way to integrate different foundation models into a single strategy, allowing organizations to choose the best model for each use case, apply common controls, and scale innovation across the organization. • Embed gen AI assistant prototypes into workflows. Place gen AI where experts already work: underwriting, credit, fraud, compliance, model risk. Let it extract, summarize, compare, draft, and recommend while humans approve and escalate exceptions. • Integrate risk management controls into the flow. Apply model risk management best practices: Validate outputs, monitor drift, log decisions, enforce privacy and access rules, and red-tag misuse. Demand explanations that auditors and regulators can understand. • Scale gen AI solutions that work. Start narrow, in a document-heavy or customer service-focused process, with clear expected ROI. Prove value in terms of speed and consistency, then extend the same architecture and governance across adjacent processes. With gen AI, risk management moves from slow, manual processing to faster, scalable, and more consistent workflows that let experts focus on decisions, not documents. Underwriters receive concise briefs with positives, negatives, and KPIs from long reports. Credit officers review decision-ready borrower snapshots. Anti-money laundering (AML) investigators see case narratives linked to evidence. Compliance officers track regulatory updates mapped to policies. Leaders gain a consolidated view of risks across silos. Faster, More Accurate Risk Assessments At Convex, an international specialty insurance company, underwriters relied on policy issuance workflows led by subject matter experts who had to process complex engineering reports before underwriters could make a decision. Each report ran a hundred pages or more, so reviewing it and preparing an underwriting summary took several hours. The process was accurate, but it meant new policies were evaluated slowly. The company’s ability to scale was tied to the volume of report processing its experts could manage. Convex joined forces with Provectus to develop AI Underwriter: a gen AI solution that can process the same reports and prepare clear, comprehensive underwriting summaries in minutes versus hours. It can highlight object details, flag positives and negatives, and extract the KPIs underwriters cared about most. AI Underwriter frees Convex experts from the grind of line-by-line manual report review and summary preparation. While Convex’s underwriters continue to review the gen AI outputs, their time is better spent on decision making instead of mundane report processing. The underwriters can now assess risks more easily, issue policies faster, and handle more business without losing quality. This type of from-report-to-insight approach may help address bottlenecks elsewhere within financial services: • Banking credit risk. Turn borrower files, filings, and correspondence into draft credit briefs and memos with citations for analyst review. • Financial crime and fraud. Assemble case narratives that link transactions, entities, and prior alerts, to improve investigator focus and documentation quality. • Compliance and conduct. Track regulatory bulletins, map them to affected policies, and draft control updates for human approval. • Model risk management. Generate first-pass documentation, summarize validation evidence, and flag drift for review. The common thread is to identify a document- or data-heavy choke point, deliver decision-ready outputs that match expert practices and streamline workflow, and keep humans accountable for final judgments. Eliminating Back-Office Bottlenecks Financial organizations face volume, velocity, and variance problems in risk management. Traditional methods can no longer keep pace with today’s exposures. gen AI solves this bottleneck by delivering faster, more consistent, and auditable insights and outputs that experts can trust. The playbook is clear: Prepare data, embed copilots, build controls, and scale proven wins. Provectus demonstrates what “good” risk management looks like: days of underwriter’s work reduced to minutes, throughput multiplied, revenue lifted—while experts and stakeholders at all levels retain oversight. For finance leaders, the takeaway is clear: gen AI can transform risk management from a back-office bottleneck to a function that shapes business strategy. gen AI enables teams to detect risks earlier, respond faster, and provide executives with a clearer view of exposures. Organizations that move now will be empowered to manage the risks of today and tomorrow more effectively, creating the capacity to pursue new opportunities with greater confidence. Looking to explore how generative AI can transform risk management in your organization? Discover how with the Provectus Generative AI solution. Helen Johnson is the Executive Director, Financial Services, Compliance and Technology Strategy at Provectus.