Disseqt AI, HCLTech and Microsoft for agentic finance

Executive Summary

• Disseqt’s lean, observable agent platform, coupled with HCLTech’s banking domain expertise and Microsoft Azure OpenAI Services, has moved agentic AI from proof‑of‑concept to production.

• Early pilots report 20–30 % cost reductions in underwriting, fraud detection, and customer service, while quantum‑accelerated portfolio optimisation offers an additional 25 % efficiency lift.

• The partnership delivers a governance‑first stack that satisfies Basel III/IV and GDPR, addressing audit trail, bias mitigation, and policy drift concerns.

• For C‑suite leaders, the key decision is whether to adopt a platform approach now or risk falling behind competitors who will leverage autonomous agents for real‑time risk assessment and customer engagement.

Agentic AI—systems that autonomously select, sequence, and execute tasks—has traditionally lived in academic labs. The Disseqt–HCLTech–Microsoft alliance demonstrates a commercial viability that reshapes the banking value chain.

• Operational Cost Reduction : Gartner forecasts a 25 % drop in manual underwriting cycles when agents handle routine queries, while Finextra’s own data suggests overall operational savings of 20–30 % across core processes.

• Speed to Market for New Products : Agents can prototype and iterate product logic faster than human teams, shortening the time from concept to launch by up to 40 %. This agility is critical as regulatory sandboxes push banks to innovate rapidly.

• Risk Management Enhancement : Simulation and red‑team testing embedded in Disseqt’s platform allows pre‑deployment bias detection and policy drift monitoring, aligning with Basel IV’s “stress test” requirements for algorithmic decision making.

• Competitive Differentiation : Banks that adopt agentic workflows can offer hyper‑personalized advisory services without expanding headcount, creating a new revenue stream through data‑driven insights.

The partnership’s architecture is modular and observable, built around three pillars:

• Agentic Core (Disseqt) : Lightweight microservices that orchestrate LLM calls (Claude 3.5 Sonnet, Gemini 1.5) via Azure OpenAI APIs. Each agent is wrapped in a policy engine that enforces compliance rules and logs every decision.

• Domain Knowledge Layer (HCLTech) : Pre‑built adapters for core banking systems (Murex, Temenos), risk engines, and KYC/AML workflows. HCLTech supplies industry‑specific ontologies that translate raw financial data into agent-friendly inputs.

• Governance & Observability (Microsoft Azure) : Azure Policy, Purview, and Sentinel provide real‑time monitoring, audit trails, and automated remediation. Data residency is managed through Azure’s multi‑region compliance certifications (ISO 27001, FedRAMP High).

A key differentiator is the platform’s ability to surface policy drift before it impacts customers. For example, an agent that historically approved loans under a certain credit score threshold will trigger an alert if its internal scoring model deviates by more than 5 %. Red‑team simulations run nightly against synthetic datasets to expose potential regulatory violations.

The pilot involved automating the mortgage approval workflow. The bank reported:

• 30 % reduction in average processing time (from 5 days to 3.5 days).

• 15 % drop in manual review hours, freeing analysts for higher‑value risk assessment.

• Zero compliance incidents during the first six months, attributed to the platform’s built‑in audit logs.

These metrics underscore the tangible business impact of agentic AI when coupled with a governance‑first mindset.

Assuming an average annual operating expense (OPEX) of $500 million in underwriting and customer service for a mid‑size bank, a 25 % efficiency gain translates to $125 million saved per year. Additional benefits include:

• Talent Reallocation : Human analysts can focus on complex risk modeling, potentially increasing revenue by 5–7 % through upselling and cross‑selling.

• Regulatory Compliance Savings : Automated audit trails reduce the need for external compliance consultants by up to 40 %, yielding $10–15 million annually in cost avoidance.

• Risk Mitigation : Early detection of policy drift reduces the probability of regulatory fines, which can be catastrophic (often exceeding $50 million for large banks).

While OpenAI’s Agentic API and Anthropic’s Claude 3.5 are still in beta for regulated use, Disseqt offers:

• End‑to‑end platform : No separate model licensing or policy enforcement layer.

• Compliance maturity : Proven track record of meeting Basel III/IV and GDPR requirements.

• Industry expertise : HCLTech’s 30‑year legacy in banking IT provides a bridge between new AI capabilities and existing core systems.

• Pilot Selection : Identify high‑volume, low‑complexity processes (e.g., KYC document extraction) as first deployment targets.

• Governance Framework Setup : Adopt AI‑PRISM or ISO 22570 standards early to align with the platform’s policy engine.

• Data Residency Strategy : Map data flows across EU, US, and APAC jurisdictions; leverage Azure’s regional compliance controls.

• Red‑Team & Simulation Cadence : Schedule monthly red‑team exercises and quarterly simulation runs to maintain audit readiness.

• Scale Incrementally : Expand from pilot to enterprise by integrating with risk engines, payment gateways, and wealth management platforms.

The convergence of agentic AI with quantum computing is poised to unlock real‑time portfolio optimisation under stress scenarios. Banks that invest in hybrid quantum‑classical pipelines can expect:

• Real‑time risk assessment : Agents can adjust exposure limits instantly as market data streams in.

• Dynamic asset allocation : Quantum annealers solve combinatorial optimization problems faster than classical solvers, enabling agents to generate optimal strategies within seconds.

• Competitive Edge : Early adopters will set new industry benchmarks for speed and accuracy, attracting high‑net‑worth clients seeking cutting‑edge investment solutions.

• Initiate a cross‑functional task force that includes compliance, risk, IT, and business units to evaluate the platform’s fit against current process maps.

• Leverage Disseqt’s observability tools to create a “policy drift dashboard” that feeds directly into your governance board.

• Integrate Microsoft Azure Purview for data lineage; this will satisfy both internal audit and external regulatory scrutiny.

• Allocate budget for a pilot that includes a dedicated agentic workflow, simulation environment, and red‑team team—estimated cost $2–3 million for a 12‑month program.

• Develop a KPI framework: measure processing time reduction, compliance incidents, and analyst productivity before and after deployment.

The Disseqt–HCLTech–Microsoft alliance represents the first fully mature agentic AI platform tailored for regulated financial services. By embedding governance, observability, and domain expertise into a single stack, banks can unlock significant operational efficiencies, accelerate product innovation, and strengthen compliance postures—all while positioning themselves ahead of competitors that will soon adopt autonomous agents to meet customer expectations and regulatory demands.

For leaders looking to future‑proof their institutions in 2025, the question is no longer

if

agentic AI should be adopted, but

when

. The next wave of banking transformation will be driven by agents that can reason, act, and self‑audit—an opportunity now within reach thanks to this strategic partnership.