Monily uses Machine Learning to take data from the email receipts and enriches the Bank transactions. 

Ageing Bank systems produce little detail on the Transactions found in Bank Statements so Customers end up calling to check for potential fraud.

By showing Customers what they purhcased they receive a better experience, Banks lower calling costs and can use the data to target personalised financial products. 

Bringing transactions to life

Ageing Systems 


Banks ageing systems do not produce enough information on transactions for modern customers.

If customers don't recognise a transaction they call the bank to check it is not fraudulent

Systems costs the Bank industry millions in call centre costs.

Email Pain


Unrecognised transactions force customers to search through Promotional, Spam and Phishing mails to find receipts.


This makes it hard to reconcile debits and credits found on their Bank accounts.

 

Email volumes make it unusable to find receipts 

Poor data

 

Lack of data means Banks cannot provide data rich experiences like Uber or Amazon.

This results in low Net Promoter Scores and not being able tailor products and offers. 

Poor data costs the banking industry billions in lost revenue. 

 

Problem

 

Solution

We mine the worlds largest receipt bank

Consumers don’t use Email to communicate anymore.

Today, Consumers use WhatsApp to chat turning Email into the worlds largest Receipt bank but it is impossible to find Receipts amongst promotional, SPAM and Phishing emails.  These Emails contain all the detail not found on Bank statements; item prices, descriptions, sizes, quantities, travel times, subscriptions, destinations, etc.

This is why Monily has developed Splinter. Aimed at Banks, Credit solutions and Financial Software, we use Machine learning on Email to extract the data from the receipt and enrich the matching transaction you find in your Bank statement. 

For the first time Customers will be able to see they bought a Red dress size Small from H&M for 19.99 or a flight time to Rome in their App, without the need to go and look at the receipt

For Consumers it is secure and simple

 

When a consumer gives permission to extract email Purchase receipts, Splinter securely takes the data out and discards the emails to Grandma, Phishing and Spam. Its completely secure and GDPR compliant. 

Our technology is based on powerful Natural Language Processing techniques such as Document Classification and Named Entity Recognition. Splinter’s custom processing engine combines machine learning algorithms, feature extraction concepts and text manipulation to solve the problem of interpreting email receipts.

 

We do this all securely either on the Smartphone or behind the Banks firewall. Splinter does not send the data to the cloud or third-parties so Consumers and Banks dont have to worry about data being hacked. 

Without Splinter

Limited detail

With Splinter

Enhanced data

Splinter is delivered onto a Smartphone or into a Banking system as Machine Learning models together with the Email and Banking data from the providers.  


The Machine Learning is then securely executed using the Smartphone's Machine learning solution (e.g. Apples Core ML) or behind the firewall using the Banks chosen AI  solution (e.g. H20). Splinter takes the form of a set of containerised services that are interoperable. We supply Docker images that you can slot into your existing orchestration architecture such as Kubernetes.

 

Benefits

Product

Seeing purchases =  less fraud enquiries and bank costs
 

Banks can offer better services like ride hailing based on purchases
 

Business expenses are automatically reconciled with receipts
 

Purchasing data can be used to assess a Credit Score
 

Up/Cross-sell based on actual purchases, e.g.  travel insurance for flight booked
 

Enriching data across sectors

Splinter's Machine Learning enhances the data used in Retail Banking, Credit Scoring and Finance Software. Each sector can use the data in different ways to enrich, differentiate and personalise financial products and services.  

New
Services

Upsell &
Cross sell

Reduce
Fraud

Reconcile
Expenses

New Services

Banks can make their Apps useful using the data. Imagine a customer books a York to London Train ticket the details appear in the App and an Uber button appears with a 10% off offer. 

 

Up and Cross Sell product

It has long since been the holy grail of marketing to be able to personalise offers based on purchases. Using the Splinter data customers booking  US holiday for the family, Travel insurance could be offered at 15% off.

Reduce Fraud

Customers seeing transactions on Bank Statements they do not recognise causes fear of Fraud resulting in 30 million calls a year. Matching receipts to transaction highlights were there are missing receipts, illustrating potential fraud. 

Reconcile Expenses

In personal finance, households find it hard to reconcile expenses across various cards. Adding detail to the transaction allows Consumers to reconcile their expenditure and their refunds

Weighting Purchasing

Traditional FICO Credit Scoring do not take into account the consumer's actual purchasing when creating a score.  Using Purchasing Patterns to augment a score allows Credit providers to give weighting to those who may not have taken credit out previously.     

Reconciling Accounts

Matching receipts to expenses is tedious and time consuming, Splinter can automatically match the receipt to the expense in Financial Accounting software saving hours of digging around email. This allows VAT to be automatically calculated and reclaimed.

Market benefits

Bank Apps & PFM solutions

Credit scoring

 

Demo

Financial Accounting Software

Monily have developed an App to demonstrate the Splinters Machine Learning capability

Shoppa has been designed using Open-banking to show how online shoppers can see item level purchases, prices, returns and refunds across all their cards. It also demonstrates to Banks the monetisation models that can be  built using the data. 

The App is one of the first on the world to securely integrate all the data onto the Smartphone using Apples Core ML capability.

  

 

About us

The idea for Monily came in Christmas 2017, our house was like a ‘Fair’, we were spending money across mulitple cards, returning goods and losing track of the refunds. Bringing together some of the brightest brains in data science and banking we set about building an answer to this problem, Monily was born. We soon realised the problem was simple, we had to combine the receipt with the banking transaction. 

​ At first we wanted to build an App, Shoppa,  that meant you didn’t have to go into your email to find the order, remember that password for the portal, label and parcel it, hoof it down to the post office and then forget to remember to check you bank account to see they sent the money back. As time when on we soon realised this was an idea that could change banking with the advent of Open Banking. 

Adrian James

CEO and Co-Founder
 

Andy McIntosh

Head of CX Co-founder
 

Martin Langosch

CTO and Co-founder
 

Dr Roman Popat

CDO and Co-founder
 

Priya Patel

CMO
 

Peter Williams

Ex Head of Analytics
Marks & Spencers

Iain Levin

Chairman
Ex MD 

Comet Consulting
 

Michael Woods

Head of Big Data

Citigroup
 

Craig Forster

Senior Partner
McDougall McQueen
 

Board Advisory

The Founders

Supported by

 

Contact us

Interested in finding out more? 

If you are a Bank, Credit company or provide Financial software, we would love to hear from you on email or leave us your number and we will call you back. 

Alternatively, hit the Let's Chat button on the right and and we can chat online. 

© 2019 Monily