One engine for every price the bank charges
Fees and interest in a single framework that reads the whole relationship.
Configured by business teams instead of IT. Explainable down to the rule that fired. And running in production today at one of India’s largest private-sector banks.
One relationship — every price assembled from it
The Problem
Eight barriers stand between banks and agile pricing
Product-Centric Pricing Models
Pricing structures tied to products instead of customer relationships, profitability or behaviour.
Fragmented Fee & Interest Systems
Separate engines for fees and interest create operational complexity, duplication and inconsistent pricing experiences.
Slow Time-to-Market
Launching new fees or interest products requires heavy IT involvement and long release cycles.
Rigid Pricing Structures
Legacy pricing models lack the flexibility to support reusable, layered and condition-driven pricing logic.
Limited Integration Flexibility
Legacy pricing systems struggle to integrate consistently across channels, processors and banking platforms.
Limited Transparency
Customers and business teams lack visibility into pricing calculations, rule evaluation and decision logic.
Weak Governance & Control
Manual overrides, inconsistent approvals and limited auditability increase operational and compliance risk.
Limited Pricing Intelligence
Banks struggle to simulate pricing impact, evaluate profitability or optimise pricing strategies proactively.
Every one of them is answered by a named component of the platform ↓
The Solution
A closer look at each capability
Click any component to see what it actually does.
Pricing that reflects the full customer relationship
Relationship and hierarchy-based pricing, with personalised experiences across every segment — configured at whichever level the bank needs it.
Relationship-driven
What the price can read
- Customer profitability
- Portfolio value
- Segment & behaviour
- Relationship size
- Customer hierarchy
- Organisation or employer
Personalised experiences
Tailored pricing for specific cohorts
- Platinum / premium customers
- Senior citizens
- Strategic cohorts
- Corporate relationships
- Employee banking programs
Hierarchy based
Configure at multiple levels, control which one applies
- Customer level
- Product level
- Cohort level
- Corporate level
- Account level
Every pricing dimension — segment, structure, individual — managed in one engine.
The Solution in Real Life
See how the system prices the relationship, not the event
Each of these seven moments is an event a legacy engine would price on its own — an account opening, a loan application, a complaint, a missed balance. Watch what changes when the bank prices the relationship behind the event instead. Pick a year.
Fees in Depth
Eight fee scenarios, and the pricing logic each one needs
Conditional rules, free limits, bouquets, negotiated deals, clawbacks and full explainability — every one drawn from a real banking requirement. Pick a scenario.
Conditional Pricing
Scenario 1 of 8Flexible rules for context-aware fee computation across customer, channel and behaviour attributes.
Business need
Fees that adapt to context
Banks need to vary fees by customer profile, account status, channel, geography or transaction behaviour. Static fee structures cannot support that flexibility.
Example — Account maintenance fee
Charge a fee only when ALL of these apply:
- Minimum monthly balance is not maintained
- Account is active
- Pricing varies by branch category
To support this, the required pricing logic is
- Urban branches: maintain $10,000 average balance
- Rural branches: maintain $5,000 average balance
- If balance falls below threshold, fee = 6% of the shortfall
- No fee for dormant accounts
ePricing supports this by enabling
- Conditional rule evaluation
- Expression-based fee computation
- Multi-attribute pricing logic
- Branch-level differentiation
- Real-time fee eligibility checks
Business value
Precise pricing
Granular control
Personalised fees
Per customer or cohort
Less manual work
Rule-driven automation
Better segmentation
Context-aware logic
Interest in Depth
Interest as a structure, not a number
Fixed, floating, benchmark-linked and dynamically repriced models — all from one framework. Every strategy stays independently configurable, and every rate is assembled from components that can be explained afterwards.
How the final rate is assembled
Not one number from one table — a stack of reusable components, validated against a floor before it is allowed to become a rate.
Running rate
0.00%
01
Benchmark Linkage
External reference rate
6.50%
02
Spread Adjustment
Risk, tenor and product
+1.25%
03
Promotional Overlay
Time-bound campaign
−0.40%
04
Relationship Benefit
Earned by the relationship
−0.25%
Validated against floor rate control
Minimum-rate safeguard set at 6.75% — the assembled rate must clear it before it can be applied.
Final interest structure
Reassembled automatically whenever any component beneath it moves.
7.10%
Illustrative figures, shown to demonstrate the assembly mechanism — not indicative pricing.
Flexible interest strategies
Move beyond static debit and credit interest processing. Each strategy is configured on its own terms and behaves on its own terms.
By design
Every strategy stays independent on four dimensions
- 01Calculation basis
- 02Applicability conditions
- 03Execution timeline
- 04Pricing behaviour
Combine spreads, overlays, benchmark linkage and floors into multi-layer rate structures — without any of them interfering with each other.
Interest Scenarios
Four ways the structure earns its keep
Risk-based lending rates, profitability guardrails, cross-product bundles and full rate explainability — each one a real requirement, each one configured rather than coded.
Risk & Relationship Based Home Loan Pricing
Scenario 1 of 4Personalised lending rates driven by customer risk and relationship value.
Business need
Rates that reflect risk and relationship
Banks want to offer differentiated home loan rates based on customer risk profile, collateral quality, relationship value and repayment behaviour — while keeping the pricing logic configurable and transparent.
Example — Personalised home loan pricing
The rate is built from:
- Base rate from FICO score and CLTV ratio
- Tenor-based adjustment plus auto-pay discount
- Loyalty step-up after 36 months, enhanced after 60
To support this, the required pricing logic is
- Multi-dimensional rate determination using risk attributes
- Component-based assembly with conditional overlays
- Tenor-based spread adjustments and event-driven discounts
- Time-bound lifecycle and relationship-based differentiation
ePricing supports this by enabling
- Decision-table base rates
- Reusable components and overlays
- Conditional behaviour pricing
- Duration-based activation and expiry
- Dynamic recomputation with component-level rate explainability
Business value
Personalised pricing
Tailored to each customer
Better margins
Risk-adjusted profitability
Improved retention
Reward loyal customers
Auditable pricing
Transparent decision logic
Adoption
A product-based migration — no big bang, no pricing outage
The hardest question about replacing a pricing engine is not whether the new one works. It is what happens to live pricing while you find out. This is how one of India’s largest private-sector banks answered it.
Step 01
Parallel Run
2 products
in Fivolv parallel to legacy for accuracy check
Step 02
Full Parallel Run
All products
in Fivolv parallel to legacy for accuracy — 6 months
Step 03
Initial Migration
2 products
migrated to Fivolv only
Step 04
Full Migration
All products
all products live on Fivolv
This approach ensures
Zero pricing disruption
Pricing operations continued without interruption.
Validation before replace
A six-month parallel run proved accuracy first.
Controlled transition
Operational handover stayed under bank control.
Incremental modernisation
Products migrated progressively at the bank's pace.
Progressive coexistence enabled complete pricing transformation without operational risk.
Proof · Proven at Scale
Alex’s story — running across 100 million customers, every day
One of the largest private-sector banks in India runs ePricing in production today.
0M+
daily transactions processed
0M+
CASA balances managed
0M+
master records handled
0M
file-upload records processed per day
0→3 hrs
SLA improvement
0×
reduction in product configs (600 → 83)
What this means in relationship pricing terms
Account-Level Negotiations at Scale
Alex's deal — replicated for millions of high-value customers — governed, auditable, repeatable.
7× Simplification Around the Relationship
600 product configs reduced to 83 — by rationalising around the customer, not the product.
Hierarchy Pricing Across Every Relationship
Personal, employer, corporate and segment pricing operating simultaneously at enterprise volume.
Governance Across 100M+ Daily Decisions
Every clawback, waiver, step-up and benefit — fully audited, fully controlled, fully explainable.
Business Outcomes
Tangible value for banks, across five dimensions
Agility & Innovation
- Faster launch of pricing strategies
- Reduced dependency on IT
- Rapid response to market changes
Customer Experience
- Personalised pricing experiences
- Transparent communication
- Improved trust and retention
Operational Efficiency
- Centralised pricing management
- Reduced configuration complexity
- Lower operational overhead
Revenue Optimisation
- Reduced leakages
- Better profitability management
- Improved pricing governance
Strategic Advantage
- Customer-centric differentiation
- Enterprise-wide consistency
- Future-ready platform
Price the relationship, not just the product
See how ePricing turns every fee, rate, waiver and benefit into a signal that the bank knows its customer — governed, explainable, and proven at enterprise scale.