"Agile Infoways team delivered exceptional iOS and Android apps with responsive support and outstanding problem-solving expertise."
- Rob Machado
IndustriesBanking & Finance
Fintech software development that cuts costs by 20-50%
Accelerate innovation with Fintech software development built for security, compliance, and growth. As a trusted Fintech software development company, we deliver scalable Fintech software development services across the United States that modernize payments, digital banking, lending, wealth management, and insurance platforms. Our experts combine AI, cloud, data, and automation to streamline operations, strengthen customer experiences, reduce operational risk, and help financial institutions adapt faster to evolving market demands and regulatory expectations.
Industry Challenges
The Pressures Facing Modern Financial Institutions
Fraud vectors are multiplying, compliance costs are escalating, legacy systems are stalling growth, and customer expectations demand instant, personalised service — all while margins remain under relentless pressure.
6 key challenges
01
Fraud & Financial Crime
Sophisticated fraud rings, account takeover attacks, and synthetic identity fraud cost global banks over $485B annually — with rule-based detection systems generating false positive rates above 90%.
02
Regulatory Compliance Burden
Basel III, MiFID II, DORA, and evolving AML directives demand continuous monitoring, complex reporting, and audit-ready documentation that consume up to 15% of operating budgets.
03
Legacy System Debt
Core banking platforms built on decades-old COBOL infrastructure cannot support real-time decisioning, open banking APIs, or the data pipelines required to compete with digital-native challengers.
04
Customer Experience Gap
Customers expect Amazon-grade personalisation and sub-second response times, while most traditional banks still offer generic products and multi-day loan decisions that drive attrition to fintechs.
05
Credit Risk Complexity
Thin-file applicants, SME borrowers, and volatile economic conditions expose traditional scoring models — limiting portfolio growth while increasing provisions for credit losses.
06
AML & KYC Bottlenecks
Manual KYC processes take 26–34 days on average, drive 80% of new customer drop-off, and leave institutions exposed to regulatory fines that exceeded $10.4B in 2023 alone.
Banking & Finance Services
Banking & Finance ServicesWe Deliver
End-to-end AI and engineering solutions for financial institutions — from fraud prevention and credit automation to core modernisation and conversational banking at enterprise scale.
Fraud Detection AI
Real-time transaction scoring and anomaly detection models that identify fraud at the point of transaction — reducing losses while keeping false positive rates below 1%.
Real-time transaction risk scoring (<50ms latency)
Behavioural biometrics & device intelligence
Graph network analysis for fraud ring detection
Adaptive model retraining on emerging fraud patterns
Regulatory Reporting Automation
Automated data collection, transformation, and submission pipelines for regulatory filings — eliminating manual spreadsheet workflows and reducing compliance ops headcount.
Automated Basel III / MiFID II report generation
Real-time regulatory data lineage & audit trails
Cross-jurisdiction compliance rule engine
Regulatory change monitoring & impact alerts
Core Banking Modernisation
Phased migration from legacy monolithic cores to API-first, cloud-native architectures — with zero-downtime cutover strategies and seamless third-party ecosystem integration.
Strangler-fig migration with parallel run validation
Open Banking API gateway development (PSD2/FDX)
Event-driven microservices architecture
Real-time data replication & reconciliation
Conversational Banking AI
Intelligent virtual assistants and AI co-pilots that handle complex customer queries, product recommendations, and financial coaching — at any hour, in any channel.
LLM-powered financial advisory chatbot
Omnichannel deployment (web, mobile, WhatsApp, IVR)
Personalised product recommendation engine
Live agent handoff with full conversation context
Credit Scoring & Underwriting AI
Next-generation credit models that incorporate alternative data signals to approve more creditworthy borrowers, reduce default rates, and compress decisioning time from days to seconds.
Alternative data integration (open banking, telco, behavioural)
Explainable AI credit decisions (ECOA/GDPR compliant)
Automated document collection & income verification
Portfolio stress-testing & vintage analysis dashboards
KYC / AML Automation
End-to-end identity verification, sanctions screening, and transaction monitoring pipelines that reduce onboarding time from weeks to minutes while strengthening AML controls.
AI-powered document verification & liveness detection
Real-time sanctions & PEP screening (OFAC, UN, EU)
Automated SAR generation & filing workflow
Continuous customer risk re-scoring engine
Use Cases
Real Results from Financial AI Deployments
How banks, lenders, and financial services firms used our AI to cut fraud losses, accelerate decisions, and unlock growth.
01Case Study
71%Fraud Loss Reduction
Fraud DetectionReal-Time MLCard Payments
Real-Time Fraud Detection Overhaul
The Challenge
A Tier 2 retail bank with $38M in annual card fraud losses and a rule-based system generating 92% false positives — burning out analyst teams and degrading customer experience.
The Outcome
ML fraud detection model deployed in 11 weeks reduced fraud losses by 71%. False positive rate dropped to 0.8%. Analyst review volume cut by 65%. Annual savings of $27M realised in year one.
02Case Study
4hrsLoan Decision Time
Credit AIAlternative DataSME Lending
AI-Powered SME Loan Decisioning
The Challenge
A commercial lender processing 4,200 SME applications monthly with a 22-day average credit decision cycle and 34% analyst rejection rate due to thin credit files.
The Outcome
Alternative data credit model reduced average decision time to 4 hours. Approval rates increased 28% with no increase in default rates. $120M in net new lending volume unlocked in 12 months.
03Case Study
9minKYC Onboarding Time
KYC AutomationIdentity VerificationOnboarding
Digital KYC Onboarding Acceleration
The Challenge
A private bank with a 31-day manual KYC process losing 80% of high-value customers before account opening — costing an estimated £18M in first-year revenue annually.
The Outcome
End-to-end AI KYC platform reduced onboarding to 9 minutes for 94% of applicants. Customer drop-off fell from 80% to 11%. Compliance team capacity freed by 60% for complex cases.
04Case Study
$2.1MAnnual Compliance Savings
RegTechBasel IIIReporting Automation
Regulatory Reporting Automation
The Challenge
A mid-market investment bank spending 1,400 person-hours per quarter on manual Basel III reporting across 12 legal entities — with recurring late submission penalties.
The Outcome
Automated reporting pipeline eliminated manual data extraction. Quarterly reporting cycle compressed from 6 weeks to 4 days. Zero late submissions in 8 consecutive quarters. FTE cost savings of $2.1M annually.
Proven Impact
The Numbers Behind Banking & Finance AI
Measurable outcomes from AI deployments across retail banks, commercial lenders, and financial services platforms.
Top Result
94%
KYC Automation RateReducing onboarding from weeks to minutes
3x
Credit Decision SpeedFrom 22-day cycles to same-day approvals
60%
Fraud Loss ReductionAverage across retail banking deployments
$4.2M
Compliance Cost SavedPer year across reporting & AML ops
Ready to Transform Banking & Finance with AI?
Book a free 45-minute AI discovery session with one of our banking & finance AI specialists.
Before You Invest
AI in Banking & Finance
What financial leaders ask before investing in AI — where to start, whether generative and agentic AI are ready, regulatory compliance, data security, fraud detection, and building versus partnering.
8 questions answered
01
Where should a bank start with AI to see ROI fastest?
Start where data is rich and the payoff is measurable — fraud detection, credit decisioning, or KYC automation usually deliver the clearest early ROI. Pick one high-volume process, prove the numbers, then expand. Anchoring the first project to a hard metric like loss reduction or decision time builds momentum, as our case studies show.
02
Is generative or agentic AI actually ready for banking, or just hype?
Both are real, but fit different jobs. Generative AI excels at drafting, summarizing, and customer conversations; agentic AI handles multi-step workflows like dispute resolution or onboarding with oversight. In regulated banking, you deploy them with guardrails and human review on high-stakes actions. Start narrow with agentic AI where errors are recoverable.
03
How do you keep AI models compliant and explainable for regulators?
Regulators expect model risk management — documented data, validation, and explainable decisions, especially for credit and AML. We build with transparent scoring, bias testing, versioning, and full audit logs aligned to frameworks like SR 11-7 and the EU AI Act. Governed AI infrastructure makes every decision traceable and defensible.
04
Is our sensitive financial and customer data safe with AI?
Yes — we deploy inside your environment or a private cloud, never sending customer data to third-party model training, with encryption, access controls, and audit logging throughout. Data residency and retention match banking regulations. Secure DevOps and infrastructure keep sensitive records contained from ingestion through deletion.
05
How is AI fraud detection better than our rule-based system?
Rules catch known patterns and flood analysts with false positives; AI learns normal behavior per customer and scores each transaction in real time, catching novel fraud rules miss. It adapts as tactics shift and pushes false positives below 1%. The engine is built on predictive models and anomaly detection, not static thresholds.
06
Should we build an in-house AI team or partner, and what does it cost?
Build in-house when AI is central and you can hire scarce senior talent; partner when you need regulated-grade expertise fast or want to ship before hiring catches up. Cost scales with scope, so most banks start with one funded use case. Many augment teams with dedicated AI/ML engineers to control spend.
07
What does fintech software development cost?
Fintech software development usually starts around GBP35k to GBP80k for a focused payments or lending product and can exceed GBP200k for multi-system platforms with fraud, compliance, and core integrations. Scope, security review, and regulatory requirements drive the range. When teams need recommendation engines or approval logic as well, we often extend the build with decision intelligence.
08
Can you build secure banking software?
Yes. We build secure banking software development projects with PCI DSS controls, SOC 2-aligned logging, encryption, role-based access, and audit-ready workflows across payments, lending, and customer servicing. That includes API platforms, mobile apps, and internal operations tools. When banks need broader product delivery beyond AI, we pair that work with custom software development.
Client Stories
From Vision to Verified Results
Behind every number - 40% faster deliveries, 60% less admin workload, 50% quicker data processing - is a client who trusted us with a real business challenge. These aren't just demos. They're live products, running at scale sustainably, delivering results clients can measure.
"Great company with great management quality developers were really dedicated to get the job done in a timely cost-effective manner."
- Alexandar Salahsour
"They consistently delivers reliable, high-quality development solutions with exceptional communication, value, and trusted partnership."
- Joe Pellegrino, Jordan Pellegrino
Get In Touch
Let's Build Something Remarkable Together
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