Industries
Olakai for Financial Services
Measure AI ROI, govern risk, and prove value across banking, insurance, and wealth management.
AI Is Transforming Finance. Measuring It Is the Next Step.
Financial institutions are investing aggressively in AI. Major banks have deployed hundreds of use cases across fraud detection, credit decisioning, and customer service. The technology works. What most institutions lack is a way to prove it.
The regulatory environment makes this gap especially painful. SOX compliance demands complete audit trails. AML and KYC regulations require documentation of how decisions are made. OCC and FDIC guidance increasingly requires explainability for AI-driven lending decisions. Meanwhile, nearly 40% of employees in financial services use AI tools not approved by their organization, each instance representing both a compliance risk and a missed opportunity to measure AI’s actual business impact.
By the Numbers
Nearly 40% of financial services employees use unsanctioned AI tools.
Every unauthorized use is a compliance risk you can’t see and ROI you can’t measure.
Example KPIs for Common Agentic AI Workflows
Financial institutions deploy AI across dozens of functions. Olakai gives you the KPIs to prove each one is working, meeting compliance standards, and delivering measurable value.
Example 1: Audit Trail Automation · Finance
Audit preparation consumes weeks of manual effort every cycle. This workflow continuously monitors financial transactions, flags outliers, gathers supporting documentation from multiple systems, and maintains audit-ready packages with complete traceability. When auditors arrive, your documentation is already organized.
Key KPIs to measure with Olakai
Audit Preparation Hours Hours from audit notification to documentation-ready status.
Finding Resolution Time Days from flagged finding to remediation.
Documentation Completeness Percentage of transactions with full audit traceability.
Example 2: Contract Review · Legal
Financial services legal teams review thousands of contracts annually. This workflow extracts key terms, flags non-standard or risky clauses against internal playbooks, and suggests redline changes. For institutions where a single missed clause in a counterparty agreement can mean millions in exposure, faster and more thorough review is a competitive advantage.
Key KPIs to measure with Olakai
Review Cycle Time Days from contract received to fully reviewed.
Risky Clauses Identified Non-standard or risky clauses caught per review.
Contracts per Attorney Monthly contract throughput per reviewer.
Example 3: Churn Risk Detection · Customer Success
In wealth management and commercial banking, losing a high-value client means losing years of relationship-building and referral potential. This workflow monitors engagement metrics, transaction patterns, and service interactions to flag at-risk clients before they move to a competitor, giving relationship managers time to intervene.
Key KPIs to measure with Olakai
Client Retention Rate Percentage of flagged at-risk clients retained after intervention.
AUM Retained Assets under management saved by early intervention.
Intervention Success Rate Ratio of successful saves to total interventions attempted.
Example 4: Incident Response · IT
When security incidents or system outages occur at a financial institution, every second counts. This workflow detects anomalies across infrastructure, gathers relevant logs, assesses severity using pattern analysis, and notifies the right team members. Incidents that used to cascade into crises get contained early.
Key KPIs to measure with Olakai
Mean Time to Detection Minutes from anomaly occurrence to alert triggered.
Mean Time to Resolution Hours from incident detection to resolution.
Severity Distribution Ratio of P1/P2/P3 incidents over time.
Example 5: Lead Qualification · Sales
Financial services sales teams need to prioritize high-value prospects from a large pool. This workflow scores leads based on firmographic data, engagement signals, and financial profile indicators, routing the highest-potential prospects to senior relationship managers while automating nurture sequences for earlier-stage leads.
Key KPIs to measure with Olakai
Lead-to-Client Conversion Percentage of AI-qualified leads that become clients.
Average Deal Size Revenue per converted lead.
Time to First Meeting Days from lead creation to first relationship manager meeting.
Why Measurement Changes Everything
From Audit Anxiety to Audit Confidence
When every AI-assisted transaction has a complete trail, audits become a formality. Your compliance team stops scrambling and starts demonstrating continuous improvement with data.
From Retention Guessing to Revenue Protection
Instead of hoping churn models work, you measure exactly how many clients were saved, how much AUM was retained, and what each intervention costs versus what it protects.
From Regulatory Lag to Proactive Compliance
Track how quickly your organization adapts to new regulatory requirements. Measure gap identification speed and remediation cycles rather than waiting for the next audit to find out.
From Shadow AI Risk to Controlled Adoption
Replace “block everything” with “measure and govern.” See which unsanctioned tools employees are using, quantify the risk, and bring high-value usage into the fold with proper oversight.
From Incident Chaos to Measured Response
When you benchmark detection and resolution times, every incident becomes a data point. Your security posture improves with evidence, not assumptions.
From Legal Bottleneck to Scalable Review
Measure contracts per attorney and risky clauses caught. Prove to the business that legal is scaling capacity without adding headcount, turning a cost center into a strategic advantage.