AIFISE vs Manual CKYC — Cost, Time & Compliance Gains

Executive summary — key outcomes at a glance

AIFISE — an AI + workflow automation approach to CKYC — transforms onboarding by reducing average CKYC processing from 7 days to a few hours, shrinking manual staff needs from 45 FTE to 3 FTE (≈93% reduction), lowering customer drop-off from 25% to <2%, eliminating manual errors (8% → 0%), and enabling full RBI alignment while recovering revenue losses. This post breaks down the math, explains the tech and compliance mechanisms, and gives a practical rollout and ROI framework for banks & fintechs.

Side-by-side metrics: what changed and why it matters

Before (Manual CKYC)

• Avg CKYC processing time: 7 days

• Manual staff: 45 FTE

• Customer drop-off: 25%

• Error rate: 8%

• Compliance risk: present

• Revenue loss: ₹15 Crores

After (AIFISE automated CKYC)

• Processing time: Under a few hours

• Staff: 3 FTE (≈93% cost reduction)

• Customer drop-off: <2%

• Manual errors: 0%

• Compliance: 100% RBI alignment

• Revenue impact: Fully captured via faster onboarding

Measured improvements — exact math

1. FTE / cost reduction

• Original FTE = 45

• New FTE = 3

• Reduction = 45 − 3 = 42 FTE.

• Percentage reduction = (42 ÷ 45) × 100.

  • 42 ÷ 45 = 0.933333...

  • 0.933333... × 100 = 93.333...% ≈ 93% cost reduction.

2. Processing time (example calculation)

• 7 days = 7 × 24 = 168 hours.

• If “a few hours” ≈ 4 hours, reduction = 168 − 4 = 164 hours.

• Percentage reduction = (164 ÷ 168) × 100 = 0.97619 × 100 = 97.62% reduction (approx).

3. Drop-off improvement

• Original drop-off = 25%

• New drop-off = 2% (use upper bound of “<2%”)

• Absolute improvement = 25% − 2% = 23 percentage points.

• Relative reduction = (23 ÷ 25) × 100 = 0.92 × 100 = 92% reduction in drop-off.

4. Error rate

• Manual errors: 8% → 0%

• Reduction = (8% ÷ 8%) × 100 = 100% elimination of manual error sources (note: system-level errors still must be measured).

Why these gains matter — business impact

1. Revenue recovery — reducing drop-off from 25% to <2% means a dramatically higher conversion of leads into active customers. If previous drop-off caused ₹15 Crores revenue loss, a >90% reduction in drop-off can plausibly recover most of that lost revenue once onboarded customers begin transacting.

2. Cost savings — moving from 45 FTE to 3 FTE reduces payroll, overhead and management burden. Even with modest average FTE costs, the annual savings are substantial and pay back automation investments quickly.

3. Regulatory safety & speed — automated rule engines and audit trails make RBI alignment repeatable and defensible; faster onboarding reduces float and improves customer experience, increasing lifetime value (LTV).

4. Operational resilience — automation removes human bottlenecks and reduces error rework, freeing teams for higher-value tasks (fraud investigation, exception handling).

How AIFISE-like automation achieves this

Core components and how each reduces time, cost, and risk:

• Document ingestion & preprocessing: Mobile capture + image quality checks → instant acceptance or guided recapture (reduces drop-off).

• OCR + ML extraction: Field extraction with confidence scores to auto-populate CKYC forms (reduces manual typing and errors).

• KYC rules engine & sanctions screening: Deterministic and probabilistic checks (RBI policy rules, name matching, watchlists).

• Liveness & biometric verification: Reduces identity fraud and manual verification escalations.

• Automated exception workflows: Human-in-the-loop only for low-confidence cases (reduces FTE by focusing human attention).

• Audit trails & immutable logs: For compliance & dispute resolution (key for RBI audits).

• Integration layer (APIs): Real-time sync with CKYC registries, core banking systems, and CRM.

• Analytics & monitoring: Dashboards for drop-off, TAT, error categories and compliance KPIs.

Measuring ROI — a simple model

Inputs: average revenue per customer (ARPC), monthly new applicants, current drop-off, automation cost, average FTE cost.
Example (illustrative):

• Monthly applicants = 50,000

• ARPC = ₹10,000 (annualized)

• Current drop-off = 25% → retained = 37,500 customers.

• After automation drop-off = 2% → retained = 49,000 customers.

• Incremental retained = 49,000 − 37,500 = 11,500 customers.

• Incremental revenue (annual) = 11,500 × ₹10,000 = ₹115,000,000 (₹11.5 Crore).

• Combine with cost savings from FTE reduction and reduced rework to estimate payback period (usually <12–18 months for similar cases).

Note: you should plug in your real ARPC and applicant numbers. Above demonstrates the methodology.

Risks, caveats & mitigations

• False negatives/positives from automation — mitigate with hybrid workflow and manual QA sampling.

• Regulatory change — maintain a modular rules engine and a rapid change process.

• Data privacy — ensure encryption, least privilege access, and clear retention policies.

• Vendor lock-in — prefer standards-based APIs and exportable model artifacts.

KPIs & dashboards to track

• Mean TAT (time to complete CKYC)

• % of applications moved to automated approval vs escalated

• Customer drop-off rate (by step / device / channel)

• Manual errors or exceptions per 1,000 records

• FTE equivalent workload saved

• Cost per onboarded customer

• Audit & compliance exceptions

FAQ

Q: How much faster is automated CKYC vs manual?
A: In our example, average processing reduced from 7 days (≈168 hrs) to a few hours — >97% faster in practice depending on acceptance thresholds.

Q: Can automation fully replace humans in CKYC?
A: No — best practice uses automation for high-confidence decisions and human review for exceptions and policy updates.

Q: Is automated CKYC compliant with RBI?
A: Yes — if the system provides auditable logs, verifiable identity evidence (liveness, biometrics) and a rules engine that maps to RBI regulations.

Q: What ROI can I expect?
A: Typical payback for full CKYC automation projects is 6–18 months when factoring FTE reduction, recovered revenue from lower drop-off, and lower rework costs.