Make complex financial knowledge easier to understand, safer to apply and faster to act on.
Banks, lenders and insurers run on obligations that arrive from outside, policies that implement them, procedures that carry them to the floor, and decisions that have to be explainable years later.
ClearMash is a significant partner in our ability to connect ONE ZERO’s innovation with the service customers actually receive.
ClearMash has revolutionized the way we operate by enabling our employees to access vital information effortlessly, thereby significantly enhancing their efficiency and accountability.
ClearMash stands out as a comprehensive solution for anyone looking to access, engage with, and visualize information with ease.
The knowledge is wider than the product
The catalog is the visible layer. Products are defined by their qualifications rather than their headline, terms move, and a product withdrawn from sale is still serviced: the rule for an agreement opened years ago is not the rule for one opened this week.
Underneath it is an obligation library. A requirement arrives from outside with an effective date nobody here chose, a policy is written to satisfy it, and a procedure is written to perform the policy. The policy and the procedure are different documents with different owners and different review cycles, and the distance between them is where practice drifts.
Then there are the rules about the conversation. What may be said depends on who is being spoken to; a recommendation has to be suitable when it is made and documented well enough to be reviewed later; due diligence has steps that cannot be reordered; and a complaint starts a clock that runs whatever else the week contains.
The same position then has to exist in several forms at once: a branch conversation, an answer in the contact center, a relationship manager’s briefing, a digital journey and an AI agent, each with its own update cycle.
All of it is examined afterwards, which turns an ordinary question into a dated one: what the rule was on the day the decision was taken, and who approved it.
What a bank or lender keeps in its Brain:
- Product terms by vintage, current and withdrawn
- Rate and fee schedules, with their effective dates
- Eligibility and lending criteria
- The policy behind each obligation
- The procedure that performs each policy
- Customer due diligence steps
- Suitability and disclosure wording
- Delegated authorities and approval limits
- Account opening and closure procedures
- Complaint procedures with their clocks
- Arrears, hardship and collections procedures
- Fraud and scam response steps
What it costs when the knowledge is unclear
Inconsistent treatment of similar customers is the risk supervisors care about most. Each individual decision looked reasonable; in aggregate they show variation nobody intended and nobody can explain.
The operational costs arrive first: referrals to product specialists, handling time spent verifying, remediation when a condition surfaces late, complaints that begin with an expectation set by a superseded term, and the reconstruction work of proving what applied on a past date.
Referral queues carry a workforce cost: they are largely made of questions with definite answers, so the specialists who should be working on genuinely complex cases spend their week confirming eligibility rules.
- The challenge:
Inconsistent decisions
The same circumstances treated differently from one channel or region to the next.
With ClearMash Brain:Consistent treatment
Comparable customers receive comparable outcomes across channels and regions.
- The challenge:
Procedure behind policy
An approved change that has not yet reached the step somebody performs.
With ClearMash Brain:Change that lands before the date
An obligation reaches the procedure on the floor with its effective date intact.
- The challenge:
Evidence assembled after the fact
Demonstrating what applied at the time becomes a reconstruction.
With ClearMash Brain:Evidence as a by-product
What applied, when, and who approved it already exists.
AI raises the stakes here first
Financial services is putting AI into customer-facing work under close supervision. An AI agent handed a policy library and no defined method will apply it approximately, at volume, and every one of those decisions is examined afterwards.
What makes AI viable here is governed knowledge: the approved position, the procedure that performs it, and a record of what the organization said and when.
Which cases an AI agent may complete, which go to a qualified advisor, and what has to be recorded on the way: that boundary is organizational knowledge.
What a financial decision has to rest on
Each one is settled before a recommendation can be made and defended.
- WHATWhat product, policy or obligation applies to this customer’s circumstances.
- WHICHWhich variant or version governs the agreement they actually hold.
- WHOWho is eligible, who may be told what, and who approves an exception.
- WHENWhen the rule took effect, and whether it reaches agreements signed before that date.
- WHYWhy this is the recommendation, in terms that can go in writing.
- HOWHow the case is handled, step by step, including the exception.
The rule changed in April. Half the book was written before that.
Both populations are treated correctly, and it shows.
A rule takes effect on the first of April, and the book splits at that date: agreements written before it keep the rule they were written under, agreements written after it take the new one. Both populations are handled correctly at the same counter, and the reason goes on the file.
What Products · Versions · Eligibility
- Product terms
- Rule from 1 Apr
- Rate table
- Exclusions
- Eligibility
- Disclosure
- Legacy holding
- Effective date
- Fee schedule
- Approval limit
- Claim rules
How Suitability · Steps · Evidence
- Suitability
- Comparison
- Disclose first
- Evidence to log
- Approval path
- Exception route
- Record the case
- Complaint check
- AI agent boundary
- Handover step
- Review trigger
Context Decides which What and which How to act on, for this customer
- Who This customer
- When Signed before
- Where Region
- Which Version held
- Why Why suitable
- Frontline staffone figure, every channel
- Customersconditions before they bite
- AI in channelsapproved knowledge, clear stops
- The reason goes on the file
Tangible impact
The changes a working Brain makes, in the order a branch, a contact center and a relationship manager meet them.
Business impact
- Reduction in compliance violations 14%
- Reduction in expert escalations 65%
- Reduction in inquiry handling time 28%
- Increase in customer satisfaction 15%
- Increase in first contact resolution (FCR) 40%
- Reduction in new employee onboarding 70%
- Increase in self-service adoption 30%
- Increase in employee satisfaction 62%
Technological impact
- AI agent accuracy from 85% to 97%
- Reduction in AI costs 74%
- Increase in AI agent speed 25%
Customer result
At ONE ZERO bank, requests to experts to clarify professional information fell 41%. Read the full ONE ZERO bank story
Additional measures
Beside those figures, the measures an institution already reports on:
- Handling time and referral rate
- time from an effective date to the procedure in use
- complaint volume by cause, and complaints closed inside the clock
- remediation volume
- audit and review findings on procedure
- variation between channels on the same question
Built for supervised environments
Knowledge that shapes customer outcomes needs ownership, approval, version history and access control, and it needs them to be demonstrable. The organization decides what it says. Security detail and current certifications are published on the Security page.
See an obligation reach the step a case requires
Book a demo and we will show you a rule with its effective date reaching the policy, the procedure and the answer given at the branch, with the record it leaves.
Prefer to start with the numbers? The impact estimator works them out