The rapid adoption of AI across financial services has created unprecedented opportunities, but also amplified risks. Solytics Partners is helping institutions harness these technologies responsibly, embedding governance directly into everyday operations while reducing operational friction and enhancing regulatory confidence.
As part of FinTech Global’s prestigious RegTech100, Vikas Tyagi, CEO of Solytics Partners, explains how the firm is helping organisations navigate the complex intersection of AI, risk, and financial crime.
How Solytics deploys AI
Solytics Partners was founded to address a clear gap in the market. Tyagi explains: “The motivation behind starting Solytics Partners came from seeing how rising regulatory expectations were outpacing the capabilities of existing solutions.
Institutions were forced to rely on tools that were expensive to deploy, slow to implement, and heavily dependent on specialised support, yet still left critical risk and compliance workflows fragmented and manual.”
The firm’s goal was to create platforms that are both highly configurable and scalable, able to tackle institution-wide challenges without the typical implementation burden.
“Solytics Partners was founded to close that gap. We set out to help institutions modernise risk and compliance with tools that reduce complexity, accelerate outcomes and enable responsible, confident use of AI and analytics across the enterprise.”
The practical impact of this approach is clear. By embedding AI governance into workflows, institutions gain faster, more confident deployment of models, while ensuring compliance and regulatory oversight.
Tyagi explains “We’re seeing the strongest impact in banks, insurers and FinTechs that need to operationalise AI and GenAI governance at scale—particularly in model risk and AI governance, financial crime and AML, and GenAI safety and monitoring.”
The platform also enables a shift from fragmented, manual oversight to a single, auditable system. “By turning governance requirements into repeatable workflows, the platform enables faster, more confident deployment of AI with significantly stronger oversight, transparency and regulatory comfort,” Tyagi says.
Ultimately, Solytics Partners positions AI as a strategic enabler for compliance and business outcomes.
How AI and GenAI are transforming the wider industry
AI and GenAI are revolutionising the way institutions approach financial crime and risk management, offering faster detection, smarter risk assessments, and automated workflows.
But the technology comes with inherent challenges. Opening up on what he sees in the market today, Tyagi explains, “We’re seeing two competing forces at play: the scale of risks and the scale of opportunity. On one side, institutions need to keep up with the speed and complexity of fraud, cyber-crime and model-driven processes. Traditional rules and analytical models struggle with new behaviour patterns, real-time decisioning and data volumes.”
At the same time, firms face an increasing pressure to use AI responsibly.
“The risks we hear about from our clients are model drift, bias, explainability, PII leakage, operational resilience and the lack of auditability when AI goes into production,” says Tyagi. “These are no longer theoretical challenges—they impact regulatory outcomes, customer trust and business continuity.”
Tyagi emphasises that the opportunity lies in marrying effectiveness with governance.
“AI that is effective and governed, not just powerful. That is where the industry is heading.” Financial institutions that embrace this approach can detect anomalies earlier, reduce operational inefficiencies, and gain a strategic advantage while remaining compliant.
Embedding responsible AI
The shift toward AI-driven decision-making requires more than technology. It demands robust governance, combining technical controls with organisational alignment.
Tyagi stresses the importance of this dual approach, “The answer is both technical and organisational. From a technical standpoint, institutions need clear model lineage and documentation, scenario testing and bias checks, guardrails for GenAI, and monitoring that captures drift, performance and hallucination.”
On the organisational front, governance must be shared across risk, analytics, and business teams, rather than siloed within data science departments.
“The most successful firms we work with treat explainability and transparency as part of their design principles, not as artifacts produced at the end. If a model can’t be explained, measured or stress-tested, it shouldn’t be in production. That is where we see the industry going,” he explains.
Solytics Partners builds governance into the full AI lifecycle through three crucial elements. The first is a unified enterprise AI and model risk management framework.
“Institutions need a single, integrated approach for traditional models, ML, and GenAI—not three separate programs. This means one taxonomy, one lifecycle, one approach to risk tiering, and one consolidated view of model and AI risk across the enterprise.”
The second element is embedded controls and guardrails. Responsible AI requires clearly defined allowed and prohibited use-cases that align with regulatory and ethical expectations.
Controls are applied throughout model design, validation, approvals, change management, and continuous monitoring. Tyagi explains: “These guardrails ensure AI operates within predefined boundaries and remains consistently safe, fair, and compliant.”
The third element is tooling to operationalise governance at scale. Policies and committees alone cannot keep pace with modern AI workloads.
“Effective AI governance depends on platforms that support model and AI inventories, workflow orchestration, documentation automation, explainability, monitoring dashboards, and real-time oversight. Tooling transforms governance from a manual, spreadsheet-driven task into a repeatable and auditable operational discipline.”
Tyagi underscores the result of this approach. “When these components come together, responsible AI evolves from a one-time project into a continuous, scalable, and institution-wide governance process—one that delivers trust, transparency, and ongoing regulatory assurance.”
The future of AI
Financial crime detection and investigation is being reshaped by AI and GenAI, driving three key changes. First, risk detection is becoming far more proactive and intelligence-led, with AI systems surfacing anomalies, behavioural deviations, and emerging typologies earlier than ever before, shifting operations from reactive alerting to predictive insight.
Second, manual investigation efforts are being dramatically reduced. Natural-language and Agentic AI models can automate large parts of screening, documentation, case summarisation, alert resolution, disposition drafting, and KYC review, freeing analysts to focus on higher-value, judgement-based work.
Third, risk signals across the enterprise are becoming integrated. AI is connecting data from financial crime, fraud, cyber, AML, and credit-risk systems that historically operated in silos, enabling a unified view of risk and faster, more accurate decision-making.
In essence, AI is shifting financial crime operations from a rules-based, reactive model to an intelligence-driven, autonomously assisted paradigm. Tyagi says: “It improves accuracy, reduces cost, and materially enhances regulatory assurance.”
As financial crime and risk functions evolve, Tyagi sees a future where AI and GenAI are central to enterprise operations. The combination of predictive detection, automation, and unified risk signals transforms how institutions manage fraud, compliance, and operational risk.
“AI and GenAI are not a bolt-on or experimental technology,” he says. “They are core to how organisations will operate in the future. Getting governance, explainability and operational discipline right now is critical to avoiding regulatory friction, safeguarding trust, and unlocking the full potential of AI across the enterprise.”
This transformation presents both a challenge and an opportunity for institutions. Harnessing AI and GenAI effectively requires not only advanced technology but also robust governance, operational discipline, and regulatory alignment.
Firms that embrace these capabilities can turn financial crime and risk management into a strategic advantage, using intelligence-driven insights to reduce exposure, improve efficiency, and build trust across the enterprise.
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