30–50%
False-positive alert reduction
Real-time
Sanctions screening integration
FinCEN CDD
Rule compliant data model
40–60%
Investigator time reduction per alert
Client Snapshot
Industry
Banking & Financial Services
Solution
Data Solutions | AI, GenAI & ML
Complexity
High
Delivery
Implementation + Integration
The Problem
BSA/AML compliance is one of the most data-intensive obligations in regulated financial services. FinCEN expects institutions to maintain current CDD data, execute transaction monitoring across all product channels, and file SARs within defined timeframes. The data infrastructure underlying AML programs at most mid-tier institutions is fragmented: transaction data siloed across multiple core systems, CDD data spread across onboarding, KYC refresh, and CRM platforms, and sanctions screening data not reconciled with customer records in real time.
The majority of false-positive AML alerts are driven by data quality failures incomplete customer profiles, stale sanctions screening matches, and missing transaction context. Addressing data quality at the source reduces alert noise faster and more durably than model tuning alone. FinCEN enforcement actions for BSA/AML deficiencies regularly reach nine-figure amounts for major institutions and the root cause in most consent orders is inadequate supporting data infrastructure, not intentional non-compliance.
Ready to Start?
Schedule a BSA/AML Data Infrastructure Assessment
$553M
How PiTech Delivers
01
Multi-Channel Transaction Data Integration
02
Unified Customer Risk Profile
CDD, KYC refresh, beneficial ownership, and behavioral data combined into a single customer risk profile. Beneficial ownership data modeled to satisfy FinCEN CDD Rule requirements legal entity hierarchy, UBO identification, and ownership percentage from onboarding and periodic refresh workflows.
03
Real-Time Screening and Adverse Media Integration
OFAC and global sanctions screening connected in real time with customer record linkage. Adverse media and negative news API integration with ongoing monitoring through the customer lifecycle replacing batch-processed screening lists that are stale within hours of update.
04
Pre-Investigation Enrichment Pipeline
Alert investigation templates pre-populated with relevant customer, transaction, relationship, and screening data before the alert reaches an investigator. Investigators review structured context and make judgment calls not spend time manually assembling data before investigation can begin.
Proven Outcomes
30–50%
False-positive alert reduction through data quality improvements
40–60%
Investigator time reduction per alert through enrichment automation
18+ yrs
Banking technology experience FinCEN, BSA/AML, and data engineering depth
Proven Outcomes
18+
Years in Regulated Industries
What You Gain
30–50%
False-positive alert reduction through data quality at the source
Real-time
Sanctions screening replacing batch processing with stale match lists
FinCEN
CDD Rule compliant beneficial ownership data model
40–60%
Investigator time reduction per alert through pre-investigation enrichment
What's Included
Multi-channel transaction integration
Multi-channel transaction integration
Unified customer risk profile
Unified customer risk profile
Real-time OFAC sanctions screening
Real-time OFAC sanctions screening
Adverse media API integration
Adverse media API integration
Transaction monitoring data model
Transaction monitoring data model
Alert pre-enrichment pipeline
Alert pre-enrichment pipeline
FinCEN reporting pipeline
FinCEN reporting pipeline
Frequently Asked Questions
Which transaction monitoring platforms does PiTech have integration experience with?
How does improved data engineering reduce AML false positives without model retuning?
Most false positives arise from data gaps: incomplete customer profiles, missing transaction context, and stale screening matches that produce alerts for cleared entities. Addressing these at the data layer reduces alert noise before model tuning is required producing faster and more durable improvements than model adjustment alone, because the root cause is data quality, not model quality.
How does PiTech handle beneficial ownership data under the FinCEN CDD Rule?
What is the timeline for an AML data engineering engagement?
A focused engagement integrating three to five source systems into a unified transaction monitoring data model typically runs 6–10 months from discovery to production. Broader programs covering full channel integration, unified customer risk profiles, and real-time screening run 12–18 months depending on source system complexity and data quality starting point.
Can PiTech improve our AML program data without replacing our existing core banking system?
Yes. PiTech architects the AML data infrastructure as an extraction and integration layer that pulls from existing core banking, lending, and payment systems via API or data feeds without requiring core system replacement. The majority of AML data engineering improvements are achievable alongside existing core platforms.
AI governance is not optional in 2026. PiTech builds programs that satisfy examiners, protect customers, and enable continued adoption.
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Address
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Contact Details
(919) 439-3163
