We build AI systems that speed up credit decisions, reduce false-positive fraud alerts, and lower the operating cost of manual review, all engineered to run inside your existing core. This is what ai for finance looks like when it's built to deliver results, not just run a demo.
Trusted by 100+ businesses and AI-native startups
The future of ai in finance won't be decided by which institution deploys models fastest. It will be decided by which ones get AI past the pilot stage and into production reliably.
66%
of AI vendors cite data availability and quality as the leading barrier to AI adoption in financial services.
Cambridge Judge Business School, 2026
64.8%
of finance organizations report mixed or unsuccessful outcomes from their AI initiatives so far.
Auditoria.AI State of AI Automation, 2026
35%
of automated transaction-monitoring alerts turn out to be false positives, burying investigators in noise.
AML industry statistics roundup, 2026
$180B+
spent globally each year on financial crime compliance, much of it on work AI can materially reduce.
Flagright, The High Cost of AML Non-Compliance, 2026
The workflows differ across banking, lending, wealth, and insurance, but the requirements stay the same. Every build has to handle complex documents, support explainable decisions, integrate with existing systems, and produce the audit trail your regulators expect.
Retail and commercial banking operations, from account opening through servicing and back-office processing.
Origination and credit operations across consumer, commercial, private, and specialty lending.
Advisor enablement, research, and client operations for wealth managers, RIAs, and asset managers.
Underwriting, policy, and claims operations for carriers, MGAs, and agencies.
These are the use cases of ai in finance that show measurable results fast enough to build confidence for what comes next. The real ai in finance use cases we start with on almost every engagement.
Payment monitoring teams still rely on rules and manual review, which misses subtle patterns and leaves investigators buried in alert noise. We build detection systems trained on your institution's actual transaction behavior, so the alerts reflect genuine risk instead of blunt thresholds.
This is one of the best places to start with AI in finance because the data is already structured, the ROI is measurable, and the work is tightly tied to existing compliance operations.
These aren't hypothetical inefficiencies. They're the specific, recurring costs that make ai applications in finance high-stakes rather than experimental, and the ones we hear about first on every discovery call.
Analysts spend the bulk of their week checking, chasing, and re-keying data instead of analyzing it. Vendor and customer follow-up alone now consumes 11+ hours a week for a growing share of finance teams, time that should be going into forecasting and decisions, not data entry.
Transaction monitoring systems generate volumes of alerts that are mostly noise. Investigators burn hours clearing false positives instead of chasing the small number of cases that are genuinely suspicious, and the regulatory exposure sits there the whole time.
Income verification, credit history, collateral data, and internal policy checks are rarely in one system. Every manual handoff adds days to a decision your customer expected in hours, and every day of delay is a day a competitor can win the deal instead.
Balance inquiries, transaction disputes, and policy questions clog the same queue as complex cases that actually need a human. Without a way to triage and resolve the routine ones automatically, response times slip for everyone.
New rules, updated thresholds, and jurisdiction-specific requirements arrive faster than policy teams can map them to internal controls, and 72% of firms expect that complexity to keep increasing over the next year.
Mergers, acquisitions, and years of point solutions have left most institutions with a patchwork of legacy cores, policy admin systems, and loan origination platforms that don't share data cleanly. It's the single biggest reason AI initiatives stall before they reach production.
First notice of loss, document review, and damage assessment still route through manual triage in most claims operations. Adjusters spend hours matching policy terms to claim details by hand, and every day a claim sits in queue adds to leakage risk and erodes policyholder retention.
Pulling together portfolio performance, market research, and account history for a single client review can take an advisor hours across multiple systems. That's time spent on preparation instead of the conversations that actually retain assets under management.
Six capability layers we draw on to build ai solutions for finance, combined based on what your workflow actually needs.
Support and servicing assistants that handle balance checks, transaction disputes, and policy questions end-to-end, and hand off complex cases to your team with full context attached.
Purpose-built agents for underwriting, fraud triage, and reconciliation that take structured, repeatable decisions off your team's plate while keeping a human in the loop where it matters.
One search layer across policies, loan files, compliance documentation, and internal wikis, so your team finds the right answer in seconds instead of hunting across five systems.
End-to-end automation for reconciliation, document processing, and reporting workflows, built to run inside your existing core rather than as a bolt-on tool.
Pinnasys builds decision intelligence models for credit risk, portfolio exposure, and cash-flow forecasting that update continuously instead of on a fixed monthly cycle.
We embed every fintech AI solution within your existing infrastructure. All deployments emphasize professional-grade security, explainability, and human oversight from day one.
We measure success in hours saved, false positives reduced, decision time cut, and revenue protected, not in pilots that never reached production. That is the bar every ai in fintech engagement has to clear.
~2x
Faster Applicant Risk Profiling
10+
Admin Hours Saved Weekly
90%
Faster Model Population
8x
Speed Improvement
76%
Reduced Contract Review Time
30%
Lower Legal Cost per Contract Cycle
20+
Hours/Week Eliminated
3x
Faster Decision-making Cycle
AI systems in production today, described by the teams operating them
Aviv Argaman
Founder & General Partner, Asymmetry Point Capital
Strong architectural understanding with excellent communication clarity and practical innovations (smart-split for large tables, Cohere circuit breaker, topic-shift detection) that show thoughtful production thinking.
Security and integration are the two most-cited barriers to AI adoption in finance today. We treat both as first-class requirements, not afterthoughts.
SOC 2
Security, availability, and confidentiality controls
ISO 27001
Information security management
GDPR
EU data protection and privacy
PCI DSS
Payment card data security
GLBA
US financial data privacy
SOX
Financial reporting controls
BSA / AML
Anti-money laundering compliance
Basel III
Banking capital and risk standards