Banking & Finance
Frontier AI for the institutions that move the world's money.
The financial institutions that win the next decade will be the ones that operationalise AI faster than their competitors — on fraud, on risk, on compliance, on infrastructure, and on the customer experience that determines who keeps the relationship. The ones that wait will spend it catching up.
Syntic AI partners with banks, asset managers, insurers, and fintechs on the work that actually matters: fraud detection that adapts faster than the threat, core banking migrations that used to take years, risk and compliance modeling at quant-team speed, and custom models trained on your proprietary data and deployed inside your environment. Built for the security, regulatory, and operational requirements financial institutions actually face — not the ones that look good in a demo.
Why financial institutions choose Syntic AI
The math, the data, the regulations — and a model built to handle all three.
General-purpose AI wasn't built for finance. It doesn't understand risk-weighted assets. It doesn't know your reconciliation patterns. It can't distinguish a real anomaly from a noisy weekend. It can't be dropped into a regulated environment without months of security review, compliance scoping, and architectural work that most vendors leave entirely to you.
Syntic AI is built differently. Quantitative reasoning, multi-step financial analysis, and regulatory language are first-class capabilities — not afterthoughts. Every engagement deploys inside your environment: VPC, on-prem, or air-gapped. The audit trail, data residency controls, model risk management alignment, and IP protections are built into the engagement from day one — not negotiated in at the end after the pilot is already running.
Three ways institutions work with us
From direct API access for your engineering team to a multi-year custom model partnership — the engagement model scales to the scope of the work.
API
Direct access to Syntic AI models through our enterprise API — for institutions building their own AI products, augmenting internal platforms, embedding AI into customer-facing applications, or running AI alongside existing infrastructure. Pay-as-you-go or committed-use contracts with volume pricing. SOC 2, BAA, DPA, and custom MSAs available. Your data never leaves your environment on enterprise contracts.
Platform
Syntic AI as a deployed platform for analysts, risk teams, engineering, operations, and customer-facing staff. Single sign-on, audit logging, role-based access, and integration with your existing data sources and ticketing systems.
Custom engagements
Dedicated models trained on your proprietary data — your transaction history, your fraud case outcomes, your risk models, your customer interactions, your claims data. Built and validated alongside your quant, risk, and engineering teams. Deployed inside your environment. Owned by your institution. This is a multi-year partnership, not a vendor relationship. The model we build together is yours.
What Syntic AI handles
From the trading desk to the back office — and every regulated workflow in between.
Fraud detection and AML
Custom fraud models, trained on your data
General fraud models were trained on someone else's transaction data and someone else's fraud patterns. They miss what's specific to your customer base, your channel mix, your product set, and your geography. We build dedicated models trained on your transaction history, your historical case outcomes, and your investigator decisions — surfacing the fraud your off-the-shelf tools miss, with false positive rates tuned to your actual operational capacity rather than a vendor's benchmark dataset.
Real-time scoring at scale
Models deployed inside your infrastructure, scoring transactions in milliseconds. Integrates with your existing transaction processing systems. No data leaves your environment.
AML and sanctions screening
Adverse media screening, sanctions list matching, beneficial ownership analysis, and SAR drafting, with the reasoning trail every regulator will ask for.
Adaptive threat response
Fraud patterns shift weekly. The scheme that wasn't in last quarter's training data is in this quarter's case queue. Models retrain on new confirmed case data continuously — so the model your fraud team uses in Q4 reflects Q4 patterns, not Q2 patterns from a frozen deployment. The threat surface evolves. The model does too.
Risk and quantitative work
Credit risk modeling
Build, validate, and document credit risk models that meet the standards your regulators and model risk management team actually require. PD, LGD, and EAD work scaffolded by AI, stress-tested against your scenarios, validated by your quants, and delivered with the documentation package your examiners will ask for. SR 11-7 alignment built in from the start, not retrofitted after the model is already in production.
Market risk and stress testing
VaR calculations, stressed VaR, FRTB sensitivities, and regulatory capital modeling. Scenario design, reverse stress testing, and DFAST/CCAR submission support. The work that takes a quant team weeks gets scaffolded in days — with the reasoning attached at every step so your validators can review the methodology, not just the output, before it goes to the board or the regulator.
Portfolio analytics
Multi-asset analysis, factor decomposition, attribution analysis, and scenario modeling. AI that handles the math, the data, and the explanation.
Trading and execution support
Execution analytics, TCA, signal research, and backtesting infrastructure. For desks that need quantitative work to move faster.
Compliance and regulatory
Regulatory drafting and response
Regulatory filings, examination responses, MRA and MRIA responses, internal policy documents, and SAR narratives. Every draft comes with citations to the relevant guidance, a full audit trail of what data was used, and clear flags for the judgement calls your compliance team needs to make. Your compliance officers review, refine, and sign. They stop writing from blank documents and start editing from a strong first draft — the difference between a filing that takes three weeks and one that takes three days.
Regulatory intelligence
Track guidance changes, consultation papers, enforcement actions, and supervisory expectations across the Fed, OCC, FDIC, SEC, FINRA, CFTC, FCA, PRA, ECB, BaFin, MAS, FINMA, and equivalent authorities in every jurisdiction you operate in. Route relevant changes to the compliance owner who needs to act, with a plain-English summary of what changed, what the impact is, and what needs to happen before the effective date. The work a five-person compliance ops team does manually, automated with full traceability and a documented response record.
Audit and exam support
Internal audit acceleration, exam preparation, and evidence package generation. The artifacts auditors and examiners request, produced from your actual data with the audit trail intact.
Engineering and infrastructure
Core banking modernization
Migrations off IBM z/OS, AS/400, and Unisys mainframes. COBOL to Java, Python, or Go. Core banking platform replacements. The migrations that banks have been deferring for thirty years because the risk was too high and the timeline was too long. Syntic Code reads the legacy system, maps the business logic, writes the modern equivalent function by function, and validates it against the original behavior before a single user is cut over. What traditionally took three years and $50 million takes quarters — inside your environment, with your team, with your code never leaving your VPC.
New infrastructure and platforms
Modern data platforms, real-time event systems, customer data infrastructure, and internal developer platforms. Syntic Code as the engineering partner for the platform work that defines the next decade.
Security engineering
Continuous security review, vulnerability remediation, compliance evidence generation, and incident response. Syntic Code reviews every PR, traces data flows, and surfaces the security issues SAST scanners miss.
Customer and operations
Customer service acceleration
Multilingual support across every channel — voice, chat, email, WhatsApp, and secure messaging. Complex inquiry resolution with full context from the customer's history across products and channels. Escalation routing to the right human with a full conversation summary attached. TARP, RESPA, and Regulation E disclosures handled accurately and consistently. The customer experience that defines whether a relationship stays or moves to a competitor — handled at scale, in 16 languages, with the institution's voice and compliance posture built in.
Wealth and advisor productivity
Advisors spend more time with clients and less on paperwork. Account reviews, financial plans, performance reports, and regulatory disclosures, drafted with the data attached and ready for advisor review.
Operations and back-office
Reconciliation, exception handling, document processing, and vendor management. The repetitive operational work that consumes back-office capacity, automated with audit trails.
Custom models
When your data is the moat
The hedge fund with twenty years of proprietary trading signals that have never been shared with a model provider. The bank with millions of fraud cases and the pattern recognition that took a decade to build. The insurer with claims history and actuarial data that defines the pricing edge. The payments network with transaction data at a scale that no general-purpose model was trained on. For these institutions, off-the-shelf AI doesn't move the needle — because the edge isn't in the model, it's in the data. The model has to be trained on yours. Custom is the only option that matters.
Dedicated models, trained on your data, owned by you
Syntic builds dedicated models trained on your proprietary data, deployed inside your environment, owned by your institution. This is a custom engagement, scoped per program and priced per institution.
- Trained on your transaction data, fraud history, risk models, customer interactions, and claims data, whatever your institutional edge is
- Modality-specific: tabular data, time series, text, structured documents, or mixed
- Deployed on-prem, in your VPC, or air-gapped
- Frozen and versioned for model risk management and SR 11-7 alignment
- Built and tuned alongside your quant, risk, and engineering teams as a long-term partnership
How we work with financial institutions
Five steps. Every one runs inside your environment.
No data leaves your infrastructure at any stage. Your team reviews and approves before anything advances. The engagement is yours to control.
Step 1
Scoping engagement
4 to 6 weeks. We map the use case, your data, your compliance requirements, your existing infrastructure, and your regulatory posture. The output is a written program plan with milestones, deliverables, pricing, and a delivery timeline. Fixed-price.
Step 2
Build
A dedicated team of ML researchers, quants, financial engineers, and a compliance lead. Built inside your environment. Your data never leaves your VPC.
Step 3
Validation
Model validation aligned to SR 11-7 in the US — covering conceptual soundness, ongoing monitoring, and outcomes analysis — and equivalent frameworks: PRA SS1/23 in the UK, BaFin MaRisk in Germany, and MAS guidance in Singapore. Full documentation package: model design, data lineage, performance benchmarks, backtesting results, limitations and compensating controls, and the validation evidence your model risk management team and your examiners will require. We don't hand you a model and leave. We deliver the documentation that gets it approved.
Step 4
Production deployment
Integration with your existing infrastructure. SLA-backed, with audit logging, monitoring, and ongoing retraining as your data evolves.
Step 5
Multi-year partnership
Quarterly model reviews, new use cases, retraining cycles, and regulatory change response. A long-term partnership with the institution.
Security, compliance & IP
Built for the bar financial institutions actually face
The regulatory and security requirements aren't an afterthought here. They're the starting point for every engagement.
SOC 2 Type II and ISO 27001 in progress, available on request
SR 11-7 model risk management alignment for US banks
GLBA and Reg E compliance support
PCI DSS for engagements touching cardholder data
GDPR, UK GDPR, and equivalent regional frameworks
Region-pinned data residency (US, EU, UK, APAC)
On-prem, VPC, and air-gapped deployment options
Customer data and IP never train shared models
Full audit logging: every model call, every decision, every change
SSO, SAML, SCIM, and role-based access control
BAA, DPA, and custom MSAs available
Penetration testing reports available under NDA
Validation evidence package delivered per engagement
The model behind the work
Quantitative reasoning, regulatory language, and financial analysis as first-class capabilities.
Syntic AI runs on our own frontier model — built from scratch, not licensed from a third party. For financial services, quantitative reasoning, multi-step financial analysis, regulatory language, and structured document processing are first-class capabilities — not the result of prompting a general-purpose model carefully. The model was built to handle the math, the regulation, and the explanation in a single pass.
Standard
Daily use across analysts, risk teams, compliance, operations, and customer-facing staff. Handles financial language, structured documents, and multi-step analysis out of the box.
Deep reasoning
Extended thinking for complex quantitative work, model validation, multi-step risk analysis, and regulatory drafting. Shows its full reasoning chain so your quants and validators can review the methodology, not just the conclusion.
Custom
A dedicated model trained on your institution's proprietary data, deployed inside your environment, owned by your institution. Scoped as a multi-year engagement with your quant, risk, and engineering teams.
API
Direct access to all model tiers through our enterprise API, for institutions building AI into their own products, platforms, and infrastructure.
Who we work with
If your institution moves money, manages risk, or builds financial infrastructure — the conversation starts here.
Global and regional banks. Asset managers, hedge funds, and proprietary trading firms. Insurance carriers and reinsurers. Payments and card networks. Fintechs and digital banks. Stock exchanges and clearing houses. Sovereign wealth funds and quasi-sovereign institutions. Credit bureaus and data providers.
The common thread isn't size. It's that the work is regulated, the data is proprietary, the stakes are high, and general-purpose AI — dropped in without the right security posture, the right compliance alignment, and the right deployment model — creates more risk than it removes.
Syntic AI was built for exactly that environment. If that's yours, this is where it begins.