StratLytics Bets on Decision Intelligence to Modernise Enterprise Risk Decisioning With SLERA

For most financial institutions, managing risk is no longer about generating a credit score or producing a risk report. The challenge lies in translating fragmented data, business rules, and regulatory requirements into decisions that can be executed quickly without relying on months of development work.

This is the gap Odisha-founded AI and analytics company StratLytics Consulting is addressing with SLERA (StratLytics Enterprise Risk Analytics), an AI-native enterprise risk analytics and decision intelligence platform designed to help organisations orchestrate risk decisions through a low-code, no-code environment.

Founded by industry practitioners with deep experience in banking and financial services, StratLytics positions SLERA as the orchestration layer that sits between enterprise data and business execution. The platform helps enterprises rapidly deploy decision models, automate workflows, and continuously optimise outcomes without depending heavily on IT teams.

"Our customers already have data, models, and scores," says CEO and Founder Debasmit Mohanty. "The real challenge is bringing all of those together quickly to make business decisions. That is the gap we are solving through decision intelligence."

Removing Bottleneck Between Data and Decisions

Traditional enterprise risk systems often require lengthy implementation cycles, with business teams relying on developers to configure rules, integrate data sources, and update decision policies.

Drawing from more than a decade of experience working with large financial institutions in the United States, Mohanty says he repeatedly encountered organisations that possessed sophisticated analytical models but struggled to operationalise them at speed.

He explains that business decisions often depend on combining multiple inputs—bureau scores, internal customer data, qualification criteria, product eligibility, and policy rules. Existing platforms require significant configuration and technical intervention before these inputs can be translated into production-ready decisions.

SLERA attempts to eliminate that bottleneck through a configurable decision orchestration layer. Enterprises connect existing systems through APIs or batch files, after which business rules can be managed visually without extensive coding.

According to Mohanty, consultants perform the initial data mapping, after which business users can modify policies through a drag-and-drop interface instead of writing code. "It is low code for our consultants and effectively no code for customers," he says.

Deployment is significantly faster, with implementations usually taking one to two weeks instead of several months, assuming data and interfaces are readily available. Full enterprise deployment may require additional time for integration, security, validation, testing, and internal approvals.

Platform Built Around Governance

Unlike legacy enterprise risk platforms that have evolved over decades, SLERA has been developed as an AI-native product from the ground up.

The platform incorporates agentic AI into its architecture, ensuring effective governance, observability, and the depersonalization of personally identifiable information. Sensitive data can be masked, redacted, or tokenised in line with the financial institution's security and data-handling policies. 

Although it might suggest autonomous decision-making, SLERA selectively employs agentic AI for workflows and recommendations, while still relying on deterministic rules and requiring human approval as essential components of the process.

The platform structures complex decision workflows using directed execution pipelines, allowing tasks to run sequentially, in parallel, or conditionally, depending on business requirements.

StratLytics also deliberately avoids competing with customer data platforms or marketing automation software. Instead, SLERA functions as an API-first decision engine that integrates with existing enterprise technology stacks.

"We are not competing with customer experience or customer data platforms," Mohanty says. "We provide the decision intelligence, the brain, while those platforms continue handling execution." SLERA complements core banking systems, customer data platforms, and workflow tools by serving as an intelligence and orchestration layer that connects data, models, policies, approvals, and execution systems.

To ensure data reliability, the company views each enterprise data source as an independent "signal." If datasets share a common identifier, the platform can unify them in its orchestration layer. It can also connect data signals when reliable identifiers or approved matching logic are present. SLERA retains only the operational data needed for decision-making and allows existing systems to handle enterprise data management. 

BFSI Focus

Although the underlying technology can be applied across multiple industries, StratLytics has deliberately narrowed SLERA's focus to banking, financial services, and insurance (BFSI), where complex risk decisions are made daily.

The company has also developed a separate platform, SLICE (Supply Chain Forecasting and Inventory Intelligence), aimed at intelligent supply chain and manufacturing operations. It offers industry-specific accelerators instead of generic horizontal software.

For SLERA, however, the company's primary market is not the world's largest banks.

Instead, Mohanty says StratLytics is targeting the thousands of mid-market financial institutions, particularly in the US, that often lack dedicated AI teams, global capability centres, or advanced analytics capabilities despite managing substantial balance sheets.

He believes these organisations can benefit from AI-driven decision intelligence across acquisition, underwriting, pricing, portfolio management, model monitoring and governance, where faster experimentation and more consistent decisions can improve both business and risk outcomes.

SLERA integrates model development, validation, and governance with its decision engine, enabling the direct deployment of machine learning and deep learning models, including statistical models, machine-learning models, rules, scorecards and decision strategies, into operational workflows. It aligns with the three lines of defence: Monitor aids first-line performance monitoring and alerts; Govern supports second-line model risk management and validation workflows; and Audit facilitates third-line review and regulatory compliance, enhancing overall efficiency and risk management.

Building Through Customer Validation

StratLytics is currently prioritising customer feedback for SLERA over aggressive feature expansion.

The company is currently working with early enterprise adopters through paid pilots and implementation engagements, using their feedback to shape future product development. Planned enhancements include deeper integrations with core banking, lending, CRM, enterprise data and model-governance platforms, along with broader ecosystem interoperability, although Mohanty says only commonly requested capabilities will become part of the core platform.

Rather than attempting to replace existing enterprise software, StratLytics' strategy is to become the intelligence layer that connects them, helping organisations move from analysing risk to acting on it.

For enterprises grappling with increasingly complex regulatory environments and fragmented technology stacks, that shift from static risk management to real-time and batch decision intelligence could prove to be the company's strongest differentiator.