Tandem Bank is building a customer-obsessed digital banking business in a regulated industry. Rather than focusing first on licensing and regulations, Tandem prioritized customers by starting with their needs and feedback. Tandem developed a banking platform and app that aggregates customers' financial accounts to analyze transactions and offer personalized solutions like better savings rates or credit cards. Tandem uses AWS services like Lambda, Kinesis, and RDS to power machine learning models that provide savings recommendations to customers in a personalized, scalable way. Lessons learned include starting with customers, testing ideas quickly, and favoring customer outcomes over technology.

1. Building a Customer Obsessed Business in a
Regulated Industry: Tandem Bank's Story
Difa Niculescu – Head of IT
October 2018

3. How do you set up:
Getting started
License Beta Capital Customers
Founder Capital Team Alpha Capital Beta Customers
Capital Application Beta Capital License
with
restriction
Capital Full
License
Customers
Tech Company
FinTech
Bank
CapitalFounder Capital Team Alpha
Founder Capital Team Alpha

4. The recipe is actually
pretty simple…
The recipe is actually pretty simple…
Getting started

5. How do you flip this process if you’re customer-obsessed?
Customers
Founders Capital
Customer
committee
Team
Customers
Tech product
Hackathon
Customers
Crowd-funding
Build and break the banking model
Customers

6. Make your customers the why of your business…
Build and break the banking model
Start with a different
question:
How can we maximize
“Customer Lifetime
Benefit”?
Customers

7. “How can we optimise your finances – forever”?
7
“Make it no hassle”“Keep on top of it for me”“Personalise for me”
We asked co-founders:

8. 8
O U R P R O P O S I T I O N
Tandem proposes a uniquely differentiated digital banking offering
Retail banking product suite
Tandem
app
Credit cards
Personal loans
Deposits
Mortgages
Current accounts
Direct product
marketing through
digital channels
Net interest margin and fee income
Platform
Account aggregation
App revenue
Spending Money
Personal Highlights
Savings Coach

9. O U R P R O P O S I T I O N
Tandem platform drives daily interaction through customer savings
**Assuming switch every 4 years
Sources: Moneysupermarket.com; moneyfacts; thisismoney.co.uk; independent.co.uk
1 Download
2 Aggregate accounts
3 Analyse
4 Offer solutions
• Available in the App Store / Google Play
• Access account information from current accounts held by
other UK banks (through APIs / screen scraping)
• No need to have a Tandem current account
• Tandem analyses current and past transactions on an ongoing
basis
• Save customers money (e.g. utility bills: compare with current
market rates and offer switching services with commissions
shared with Tandem customers)
• Solve customers’ problems using Tandem’s products (e.g.
offering savings account for travel or a Tandem Credit Card)
• Relieve customers’ stress by ‘having their back’ (e.g. notify
about forgotten subscriptions and duplicated transactions)
Customer journey
• Platform looks after users’ finances, highlighting issues and money
wasted and suggests actions they can take to improve the situation
• Six switching journeys have been set up within the app with further
journeys to be added
Illustrative customer savings
Category
Annual savings
from switching
9

10. A W S F O R D A T A S C I E N C E
10
AWS LambdaAmazon Kinesis Amazon RDS
Simple model deployment – Spending Money
1 2
3
4 4
1
1. Kinesis stream contain Trigger Event (i.e. updated data)
2. Supplementary data pulled from database
3. Model runs on AWS Lambda
4. Results stored to database / published back to Kinesis
stream
5. App services read result event from same Kinesis stream
5

11. O U T C O M E S
11
Customer acquisition

12. L E S S O N S L E A R N T
12
AWS and otherwise
Outcomes
• Start with your customers
• Test ideas with them quickly
• Without customers you have nothing
• Always favour customer outcomes over tech
Tech
• Learn from AWS and their experiences
• Understand your commercial options with AWS
• Build out your V1 Tech MVP as you go
• Keep it appropriate, don’t overengineer

13. W H A T ’ S N E X T
13
Small LambdaAmazon Kinesis Amazon RDS
Larger model deployment – Reading Transactions (NLP)
Amazon SQS
Large Lambdas instances running larger model in parallel

14. W H A T ’ S N E X T
14
Increased Performance

15. 15
Personalised Banking at Scale
W H A T ’ S N E X T

16. 16
Personalised Banking at Scale
W H A T ’ S N E X T

17. 17
Personalised Banking at Scale
W H A T ’ S N E X T

18. 18
Thank You
Difa.Niculescu@tandem.co.uk