Carriers we've worked with have seen up to 80% reduction in claims cycle time · 95% average fraud detection accuracy · 85% claims automation · $500K+ annual savings
10+ years serving insurance carriers. SOC 2 aligned, ISO 27001 certified, bank-grade security. Zero reported security breaches across all deployments to date.
We reduce fraud losses, automate claims, and improve loss ratios — with measurable ROI that insurance CFOs can put in a board report.
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Supporting regional and national carriers since 2012.
Generic enterprise software doesn't understand insurance operations. The consequences are measured in fraud losses, failed audits, and adjusters who quit.
Static fraud rules catch last year's schemes. By the time your IT team updates the rules, fraudsters have moved on. We build ML models that learn continuously — 95%+ detection accuracy with less than 1% false positives that waste adjuster time.
A 14-day settlement cycle costs you customers. Every day a legitimate claim sits in a queue is a policyholder searching for a new carrier. We've reduced average settlement from 14 days to 3 — with 85% of routine claims processed touchlessly.
Two underwriters reviewing the same risk reach different decisions 30% of the time. That inconsistency erodes loss ratios and creates audit exposure. Automated risk scoring eliminates human variance while improving accuracy.
If launching a new insurance product requires 6 months of IT work, you've already lost to the insurtech competitor that launched in 6 weeks. We modernize policy administration without big-bang migrations — your team ships new products while the legacy system runs.
SOC 2, state regulatory exams, and data security audits shouldn't require months of manual evidence gathering. We design audit trails and compliance controls into every system from day one — audit-ready at launch, not after.
These are production results from deployed systems — not projections or benchmarks.
Results from production deployments. Actual ROI depends on volume, process maturity, and integration complexity.
$308B
Annual US insurance fraud losses
Rule-based systems are losing this battle. ML catches what rules miss.
14 days
Average claims settlement cycle
Our clients settle in 3 days. Every day over that is a retention risk.
30%
Underwriting decision inconsistency
Two underwriters, same risk, different answer. Automation eliminates this.
$9.48M
Avg. cost of a financial data breach
One misconfigured system. One vendor without bank-grade security practices.
We've served insurance clients for 10+ years. SOC 2 aligned, bank-grade data security. Our team builds fraud detection, claims automation, and policy systems with measurable ROI—not just features.
The same security discipline that protects criminal justice records for US law enforcement agencies now protects policyholder data in your systems. We've maintained zero reported security breaches across law enforcement and insurance deployments for 18 years. That's not a marketing claim — it's an audit record.
Not because of our technology stack. Because of how we think about insurance.
When we talk to your CFO, we speak in loss ratios, combined ratios, and reserve adequacy. When we talk to your CIO, we speak in system integration and audit trails. We've worked inside carriers long enough to know which conversation you're actually having.
Static rules are table stakes. Our fraud detection uses behavioral analysis, network graph analysis, and continuous model retraining. The model gets more accurate every month — not obsolete every quarter.
Not 'system delivered on time.' We track fraud prevention rate, touchless claims percentage, average settlement time, and NPS impact. If the operational metric didn't move, we didn't succeed.
Duck Creek, Guidewire, Majesco, or a custom legacy system — we build automation layers and modern APIs on top of what you have. Your core system keeps running. New capabilities go live in parallel. No rip-and-replace risk.
Law enforcement and healthcare data security requirements are among the strictest in any industry. We've operated under CJIS and HIPAA compliance for 16+ years. That discipline is now applied to policyholder data and financial transaction security.
"85% claims automation saved us $500K annually. More importantly, our adjusters now spend time with customers, not paperwork."
Robert Huang
VP of Claims, Regional Auto Insurer
"LTK Soft's fraud detection caught schemes our manual process missed. We prevented $400K in fraudulent claims in year one."
Thomas Brennan
CIO, P&C Carrier
"6-month ROI. That's unheard of in insurance IT. The automation paid for itself faster than we projected."
Catherine Moore
CFO, Multi-Line Insurer
Result: Reduced operational costs by $500K annually while improving policyholder satisfaction from 3.2 to 4.5 stars — without increasing headcount.
85%
Claims Automated (From 20%)
⏱ ROI in 6 monthsThe Challenge:
A regional auto insurer with 50,000 policies was losing on every front simultaneously: 14-day average settlement was driving policyholder attrition, a 5–10% fraud rate was eroding loss ratios, and manual claims processing was overwhelming adjusters. Customer satisfaction had fallen to 3.2 out of 5.
The Constraint:
The insurer could not hire more adjusters — headcount was frozen. Any solution had to improve throughput with existing staff capacity and integrate with their existing policy administration system without a full replacement.
What We Built:
AI-powered end-to-end claims automation: OCR document extraction, computer vision for vehicle damage assessment, ML fraud detection scoring at intake, and automated approval workflows for qualifying claims. Real-time processing with built-in compliance controls and full audit trails.
The Result:
85%
Claims automated (was 20%)
3 days
Settlement Time (was 14)
$500K
Annual savings
4.5★
Customer satisfaction (was 3.2)
Zero reported compliance violations across all insurance deployments to date.
Insurance data requires bank-grade protection. We design for SOC 2, PCI-DSS, and industry standards:
We'll assess feasibility, compliance implications, and expected financial impact — before any implementation discussion begins.
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