Atto Context · Product preview

Turning financial datainto financial intelligence.

Atto Context reads the financial data you already have and turns it into a connected, explainable picture of the customer or the business behind it.

Talk to us about early access
Raw transaction · Illustrative
Debit
−£212.40
PAYPAL *STRTFRD
14 Mar · Card payment · On its own this tells you almost nothing.
In context · Hover a fact
MerchantA trade supplier, not retail AccountBusiness current · 1 of 3 HistoryMonthly for 14 months PatternRises spring and autumn PositionAfter payroll and VAT · 11% of outgoings
The picture · What those facts become
Stable supplier relationship Recurring operating cost Seasonal Counted in committed spend
Size the facilityPrice the riskMonitor
The shift

More financial data does not make a better decision.
Understanding it does.

For a decade the industry's problem was access. That is built: a lender can see a customer's accounts, with consent, in seconds. The problem has moved to what the data means. A feed of transactions is not understanding, and the lenders with the most data are not automatically making the best decisions.

The data records what happened. Decisions depend on
  • Is the income genuine and sustainable?
  • What is committed expenditure?
  • Is money earned, borrowed or simply moved?
  • Is the customer getting stronger or weaker?
  • What is normal for this customer?
  • What has changed?

Meaning cannot be looked up.
It has to be resolved.

How it works

One transaction.
Then everything around it.

Context reads each line alongside the merchant, the account, its history, whether it recurs and the wider financial position. The connected story becomes the reading a decision needs.

  1. 01

    Read.

    Transactions, accounts and financial behaviour, from whatever source you have.

    14 Mar · PAYPAL *STRTFRD−£212.40
  2. 02

    Connect.

    Each transaction understood with the merchant, the account, its history, whether it recurs, and the wider position.

    14 Mar · PAYPAL *STRTFRD−£212.40
    CounterpartyRecurrenceDirectionAmount behaviourTimingRelated transactionsAccount historyConfidence
  3. 03

    Understand.

    The reading a decision needs: income quality for a household, trading quality for a business, commitments, headroom, change.

    Trading revenueStable, seasonal
    CommitmentsPayroll, VAT, 3 suppliers
    ServiceabilityHeadroom in a bad quarter
    ChangeNothing material
    Show the transactions

Every insight traces back
to the transactions behind it.

The usual approach

Label each line, then build on the labels.

  1. 01Transfers labelled in isolation. A transfer out reads as expenditure, a transfer in as income.
  2. 02Income overstated.
  3. 03Affordability looks comfortable.
  4. 04A confident decision, built on the wrong picture.

Labels accumulate. Errors compound.

Context

Every answer depends on the others.

  1. 01Transfers are resolved before anything is counted.
  2. 02Income is read before commitments.
  3. 03Commitments before affordability.
  4. 04Change is read after the decision, against what is normal for this customer.

Understanding that compounds, not labels that accumulate.

Why order matters.
Two accounts, one movement.

Raw transaction · Account A
Debit
−£2,000.00
Transfer to J Smith
3 Mar · Labelled alone: expenditure.
Raw transaction · Account B
Credit
+£2,000.00
Payment from J Smith
3 Mar · Labelled alone: income.
Internal transfer

Same amount, different meaning.
Context never mistakes movement for revenue.

One infrastructure, two lenses

One infrastructure.
Two lenses.

The models, categorisation and data architecture are shared. The interpretation adapts to whether the accounts belong to a household or a trading business.

Consumer lending

See the financial rhythm.
Understand the person behind the payments.

A score, or a categorised feed, says what happened. It does not say what it means for the person in front of you. Context reads how the household's money actually moves and resolves it into the answers a credit team needs.

  • Current account
  • Second current account
  • Credit card
  • The account they did not share
  • Context reads all of them together.
01
Income provenance
Is this income real, and will it be there next month?
Every credit read for what it is: salary, benefits, a refund, or the same £2,000 moved in from an account the applicant did not share. Income that cannot be manufactured, with the transactions behind it.
02
Affordability in context
What is actually left each month?
Committed spend separated from discretionary, so affordability rests on the money that remains after rent, bills and repayments, not on a declared figure.
03
Commitment network
What are they already carrying?
Every repayment, subscription and recurring merchant across every account, including commitments a credit file does not show. The full load, seen before you add to it.
04
Resilience trajectory
Will they still be fine in a year?
The direction of travel, not a snapshot: the buffer they keep, how often the overdraft carries them, and what a shock would do. Built to keep reading once the money is out.
The question underneath

For a household:
can they truly afford it today and remain resilient tomorrow?

In closed preview with UK lenders, building on the income, affordability and categorisation intelligence they already decide on.

SME lending

Read the business behind the balance.
A business is more than a P&L.

A bank can see every transaction a business makes and still misunderstand it. The balance says what is in the account, not whether the business is trading or what happens under strain. Context reads genuine trading activity across every account and statement and resolves it into one view a credit team can lend against.

  • Business current account
  • Second business account
  • Business card
  • Uploaded statement
  • Context reads them as one business.
01
Trading revenue in context
Is the money coming in actually revenue?
A £50,000 credit can be a customer paying, a finance drawdown or the director topping up. Each one is resolved before anything counts as turnover, so the facility is sized on real trading, growth and seasonality.
02
Cash conversion
Does the business turn sales into cash it keeps?
How quickly invoices become money in the account, and how much is left after payroll, VAT and suppliers. Working capital, runway and tax timing, read from the ledger.
03
Serviceability under stress
Can it still pay you in a bad quarter?
Payroll, tax and operating commitments are the fixed load. Context measures the headroom above them, what existing finance already takes, and how much survives when revenue dips.
04
Counterparty dependency
How much of the business rests on one name?
Customer and supplier concentration, read from who actually pays and who gets paid. Growing dependence on one counterparty shows early, with the transactions that support the call.
The question underneath

For a business:
is it generating durable cash flow, and can it service finance?

In closed preview with UK SME lenders. Not consumer Context pointed at a business account.

Understanding first

Build the understanding once.
Apply any lens.

The industry keeps building another score for each use case. Context builds a rich understanding of the financial position first, then applies the lens the decision needs. One financial context supports many models, and your own models get a richer input rather than a black box.

Each layer builds on the last
  1. 00Bank statement dataYour open banking connection, Atto's, or statements reconciled through Pacioli.
  2. 01CategorisationEvery line named: counterparty, type, recurrence.
  3. 02Transfer relationshipsMoney moved between the customer's own accounts, set aside before anything is counted.
  4. 03Income and business revenueWhat is earned or traded, separated from what is borrowed, refunded or moved.
  5. 04CommitmentsThe fixed load: repayments, bills, payroll, tax and subscriptions, across every account.
  6. 05Affordability and serviceabilityWhat is actually left, and whether finance can be carried.
  7. 06ResilienceThe buffer, overdraft reliance and capacity for a shock.
  8. 07Change signalsWhat has changed for this customer, why, and on what evidence.
  9. 08Portfolio featuresBuilt so that when every customer has Context, so does the book.

An evidence-based model of the customer, resolved in order.

One understanding, many outputs

Confident predictions
need comprehensive insights.

LendingAffordabilityCreditRiskSector view

Your own models and scores sit on top. Atto gives the insight; the lender makes the decision.

After the decision

Circumstances move.
Context moves with them.

Context knows what normal looks like for this customer, so it can see when that changes. Built to keep the understanding current as evidence arrives, and to surface the changes that deserve attention, with the explanation and the transactions behind them.

  1. 01

    Understand.

    Establish the financial position now, from the transactions up.

  2. 02

    Monitor.

    Keep that understanding current as new evidence arrives, against what is normal for this customer.

  3. 03

    Alert.

    Surface the changes that matter, against thresholds you set, with the reason and the evidence.

Consumer signals
  • Income deterioration
  • New recurring commitments
  • Falling surplus
  • Rising overdraft dependence
  • Repayment behaviour
SME signals
  • Business revenue weakening
  • Growing customer concentration
  • Tax and payroll patterns
  • Cash runway
  • Debt-service pressure
Know when the metrics moved · Illustrative
Alert
Revenue concentration increased.

One counterparty now accounts for a larger share of receipts than usual for this business.

BACS£260,400.00Payroll run
DD£62,900.00Loan credit
BACS£54,240.00Stripe payments uk
Ask what changed. Every alert carries the transactions that moved it.
The book

When every customer has Context,
so does the portfolio.

Built to read how resilience is distributed, which customers and cohorts are improving or deteriorating, where dependency or exposure is building, and where a risk team or relationship manager should look next.

  • HealthHow resilience is distributed.
  • MovementWho is improving, who is deteriorating.
  • ConcentrationWhere exposure is building.
  • SignalsWhere to look next.
Interrogate it

What changed, why,
and the evidence.

An insight that says something changed is not enough. Context shows what changed, why it changed and the transactions that support the conclusion, so a credit team can challenge it and a validator can see the working.

Every insight comes back with
  1. 01

    The reading.

    What this line means for this customer, resolved against everything around it, not labelled in isolation.

  2. 02

    The evidence.

    The transactions behind it, so your team can interrogate it and your validator can see the working.

  3. 03

    The confidence.

    A level, never a score. Your policy, your risk appetite and your models decide what to do with it.

One insight, opened up · Illustrative
Insight
Revenue concentration increased.

One counterparty now accounts for a larger share of receipts. Two others are paying later than usual.

Evidence
Trend3 monthsReceipts by counterparty
Share41% → 58%Largest counterparty
Lines23 receiptsSupporting transactions
Confidence: high. A level, never a score.

Intelligence you can interrogate
is intelligence you can put in front of a model-risk committee.

Every route to the evidence

Why stop at one data source?
Bring your own data.

Great intelligence only matters if every customer can reach it. Open banking is the cleanest route for many. Some cannot connect, some evidence sits outside the journey, and some processes still begin with documents. You keep the access layer you have; Context sits above it.

  1. 01

    Open banking.

    Atto connects the customer, or you bring the open banking connection you already have.

  2. 02

    Pacioli.

    Ledger mathematics for bank statements. An uploaded statement is rebuilt into transactions, balances and a full ledger, and reconciled until the numbers add up. Built for ledgers, not a language model.

  3. 03

    Your data.

    The statement data you already hold or receive, combined with the accounts already connected.

Your data
Connections, statements, records
Atto Context
Same categorisation engine. Same confidence.
Financial intelligence
Available to models and metrics
Bring your own data

One categorisation engine. One context layer.
More complete intelligence.

Ledger-based, not rule-based. The understanding deepens with every transaction it reads, and every output is traced back. Built to feed your models, your scores and your AI, and to enrich the bureau data and analytics you already use rather than replace them.

  • Your modelsYour workflows. Your IP. Context underneath.
  • Your bureau and analyticsEnriched, not replaced.
  • Your generative AIStructured financial intelligence as its input.
What Context returns, through Context APIs
  • Categorisation

    Every line named: counterparty, type, recurrence. The naming the rest is built on.

  • Transfer relationships

    Money moved between the customer's own accounts, set aside before anything is counted as income or spend.

  • Income and business revenue

    What is earned or traded, separated from what is borrowed, refunded or moved. Source, stability and trend.

  • Commitments

    The fixed load: repayments, bills, payroll, tax and subscriptions, across every account, including the ones a credit file does not show.

  • Affordability and serviceability

    What is actually left each month, and whether finance can be carried, in a normal quarter and a bad one.

  • Resilience

    The buffer they keep, how often the overdraft carries them, and what a shock would do.

  • Change signals

    What has changed for this customer, why, and on what evidence, against what is normal for them.

  • Portfolio features

    Built so that when every customer has Context, so does the book: health, movement, concentration and where to look next.

Context API · 01

Categorisation

Every line named: counterparty, type, recurrence. The naming the rest is built on.

Every return carries the transactions behind it and a confidence level, never a score.

The film

Atto Context
in under five minutes.

From Atto

Atto is Wrisk Group's
financial intelligence business.

UK lenders across consumer, SME, motor and property finance already decide on Atto's income, affordability and categorisation intelligence. Context is where that platform is going.

Lenders use Atto to see a customer's full financial position, including the accounts they did not think to share, and to decide on what the money shows rather than on what was declared. Atto's intelligence is used with the customer's consent at every step.

Bank-level security
  • Registered by the FCAas an Account Information Services Provider
  • Bank-level encryptionprotects all financial data
  • GDPR compliantwith an explicit user consent model
  • Read-only accessAtto never stores or retains banking credentials
  • Consent drivencomplete transparency and control for the customer
  • Soft searches onlyno impact on credit scores
See it in London

Open Banking Expo,
13 and 14 October.

Atto is a Gold Partner. Find us on the stand for the Context preview, and ask for Nick Tuttelberg, Head of Commercial.

Book a meeting at the Expo
  1. Tuesday 13 October · 15:10 · Open Finance Show stage

    SME finance: from data access to real-world outcomes

    Nimeshh Patel, Group CEO of Wrisk Group, joins the panel moderated by Joe Roche of Fintech North, alongside Open Banking Limited, HM Treasury and Allica Bank.

    View on the Expo agenda
  2. Wednesday 14 October · 14:00 · The Engine Room

    From access to understanding: open banking data in real lending decisions

    Nick Tuttelberg hosts Andrew Duncan, CEO of Soar, and Nimeshh Patel, CEO of Wrisk Group, on what a lender really does with the data.

Early access

Understand
before you decide.

Talk to us about early access to Atto Context. Tell us about your customers and your data, and we will show you the route that fits.

Or email context@atto.co.