A recommendation model that performs poorly on a retail site loses a sale. A fraud model that misclassifies transactions costs millions and erodes customer trust. A compliance reporting system that produces inaccurate output creates regulatory liability.
The consequence profile in financial services demands technology built to a higher standard of reliability, auditability, and explainability.
Our financial services-focused digital engineering practice builds AI and data systems where correctness, traceability, and compliance are engineered in, not tested in at the end.
ML models for real-time transaction scoring, account takeover detection, and synthetic identity fraud, with explainable decision outputs.
Alternative data-driven credit scoring models for underwriting, limit management, and portfolio risk monitoring.
Automated data aggregation, validation, and report generation for MiFID II, CCAR, BCBS 239, and AML reporting obligations.
Document verification automation, adverse media screening, and ongoing AML transaction monitoring AI with SAR workflow management.
Unified customer data across banking, investment, and insurance products, enabling personalized engagement and cross-sell.
Straight-through processing for loan origination, claims, trade settlement, and account servicing workflows.
SHAP and LIME explanations are attached to every model prediction, surfaced in decision audit logs and customer-facing reasoning.
Immutable logs of every automated decision with input features, model version, threshold applied, and outcome.
Version control, performance monitoring, drift detection, and documented model cards for every production model.
Model risk management documentation aligned with SR 11-7 and EBA ML guidelines.
Real-time fraud scoring system for a digital payments platform — 92% precision, 0.3% false positive rate.
Document intelligence pipeline automating identity verification for a challenger bank onboarding 10,000 customers/month.
CCAR data aggregation and report generation for a Tier 1 bank — cycle time from 12 days to 36 hours.
Alternative data credit model expanding approvals while maintaining target NPL rate for a consumer lender.
Traditional rules-based fraud detection systems apply static thresholds, for example, flag any transaction over $10,000, decline cards used in two countries within 24 hours, and require manual tuning every time fraud patterns evolve. AI fraud detection in financial services learns from millions of historical transactions to identify complex, non-linear patterns of fraudulent behavior that rules cannot express. They adapt continuously as new fraud typologies emerge, maintain low false-positive rates (typically under 0.5%), and attach explainability scores to every decision so that investigators understand why a transaction was flagged.
Yes. Our explainable AI systems are engineered with regulatory compliance as a first-class requirement. Model risk management documentation is aligned with the Federal Reserve's SR 11-7 guidance. Regulatory reporting pipelines are built to meet MiFID II transaction reporting, CCAR data aggregation, and BCBS 239 risk data aggregation standards. Every production model includes a model card, an audit trail of all predictions, SHAP-based explanations for automated decisions, and a documented validation framework.
Clients deploying our document intelligence and KYC automation platform typically reduce identity verification cycle time by 50–60% within a few months of go-live. For simple, volume-based processing, rates of 70–80% are also achievable, with outliers and higher-risk applicants routed to human reviewers. Document extraction accuracy on standard government IDs exceeds 98% under normal image quality conditions.
Our regulatory reporting automation banking platforms integrate with major core banking systems (Temenos, Thought Machine Vault, Mambu, FIS, and Finastra), as well as payment networks such as Visa, Mastercard, etc. On the data side, we work with platforms like Snowflake, Databricks, AWS FinSpace, and Azure Synapse. Secure integrations are achieved via REST APIs, message queues, and certified connector libraries depending on the system.
For explainable AI financial services, we attach SHAP (SHapley Additive exPlanations) values to every model prediction, surfacing the top contributing features behind each decision in human-readable form. These explanations are stored in immutable decision audit logs alongside the input data, model version, threshold applied, and outcome, so any automated decision can be fully reconstructed for a regulator or internal audit. We also implement model governance frameworks covering version control, performance monitoring, drift detection, and documented model cards aligned with EBA machine learning guidelines.
Yes. Our intelligent AML/KYC automation platform includes automated adverse media screening, ongoing transaction monitoring against typology-based detection rules, and SAR workflow management, including alert triage, case management, and narrative generation support. The system integrates with LexisNexis Risk, LSEG World-Check, and ComplyAdvantage for real-time sanctions and PEP screening. Alert volumes and false positive rates are tracked continuously, with model recalibration built into the operational support model to ensure monitoring remains effective as transaction patterns evolve.
Since 1999, SunTec has been a trusted business process and software engineering partner for technology companies worldwide. Our team of over 1,500 full-time employees delivers innovative services every day to help our clients save time and money.
