We present a recommender system for personalized financial advice, which we designed for a large Swiss private bank. The final recommendations produced by the system were delivered to the end clients through a mobile banking platform. The recommender system is based on a collaborative filtering technique and can work with changing asset features, operate with implicit ratings and react to explicit feedback that clients can give using the mobile app. Moreover, we developed and implemented an approach to provide an explanation for each recommendation in the form “As you bought A, you might like B".

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Recommender Systems for
Personalized Financial Advice
Aleksandra Chirkina

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BRANDSCHENKESTRASSE 41
CH-8002 ZURICH
SWITZERLAND
INFO@INCUBEGROUP.COM
INCUBEGROUP.COM
Recommender Systems for Personalized
Financial Advice
From Concept to Production
Aleksandra Chirkina
Data Scientist @ InCube

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Why recommenders for financial advice?
Why not?
Saves time and effort of Relationship Managers
Trendy
As you bought shares of Facebook
2
Twitter Google Amazon
My personal investment ideas
AIRS

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Outline
3
2
5 Integration into Banking Core Services
1 Introduction to Recommender Systems
Challenges in Finance
Latent Features and Collaborative Filtering
4 Reaction to Explicit Feedback

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Outline
3
2
5 Integration into Banking Core Services
1 Introduction to Recommender Systems
Challenges in Finance
Latent Features and Collaborative Filtering
4 Reaction to Explicit Feedback

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What is a Recommender System?
Example: Model Based Collaborative Filtering for Movies
4 5 3
1
3 4
4
5

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What is a Recommender System?
Model Based Collaborative Filtering: How to estimate missing ratings?
4 ? 5 ? 3 ?
? 1 ? ? ?
? ? ? ? ? ?
3 ? ? ? ? 4 ?
4 ? ? ? ? ?
? ? 5 ? ?
? ? ? ? ? ?

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What is a Recommender System?
Model Based Collaborative Filtering: How to estimate missing ratings?
3.8 4 1.7 5 1.3 3 3.4
3.1 1 4.4 2.9 4.2 2.1 2.2
2.8 4.3 2.8 5.0 1.6 3.4 4.5
3 2.2 1.3 3.7 1.8 4 4.6
4 3.3 1.8 4.2 2.1 3.8 3.9
4.3 4.4 2.3 5 2.8 4.5 2.9
3.3 3.8 2.7 4.1 3.1 4.4 3.8

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What is a Recommender System?
Model Based Collaborative Filtering: How to estimate missing ratings?
5 4 3.8 3.4 3 1.7 1.3
3.1 1 4.4 2.9 4.2 2.1 2.2
2.8 4.3 2.8 5.0 1.6 3.4 4.5
3 2.2 1.3 3.7 1.8 4 4.6
4 3.3 1.8 4.2 2.1 3.8 3.9
4.3 4.4 2.3 5 2.8 4.5 2.9
3.3 3.8 2.7 4.1 3.1 4.4 3.8
Top-3 Recommendations for Client A
Client A

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Outline
3
2
5 Integration into Banking Core Services
1 Introduction to Recommender Systems
Challenges in Finance
Latent Features and Collaborative Filtering
4 Reaction to Explicit Feedback

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Latent features
Model Based Collaborative Filtering: Matrix Factorization and Latent Features

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ActionRomance
Latent features
Model Based Collaborative Filtering: Matrix Factorization and Latent Features

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Model Based Collaborative Filtering: Matrix Factorization and Latent Features
ActionRomance
Escapist
Realist
Latent features

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Latent features
Model Based Collaborative Filtering: Matrix Factorization and Latent Features
ActionRomance
Drama
Comedy
Realist
Escapist

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Latent features
Model Based Collaborative Filtering: Matrix Factorization and Latent Features
Items
Users
R ≈
Users
Items
×
Shared latent features are defined for both, users and items
For users – their preferences
For items – their characteristics
Features are latent, i.e. not easily interpretable

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Outline
3
2
5 Integration into Banking Core Services
1 Introduction to Recommender Systems
Challenges in Finance
Latent Features and Collaborative Filtering
4 Reaction to Explicit Feedback

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Collaborative filtering
Adapting to clients’ feedback
Conventional Recommender System

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Collaborative filtering Finding explanations
Product
binning
Asset
selection
Adapting to clients’ feedback
Recommender System for Investment Ideas

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Collaborative filtering Finding explanations
Product
binning
Asset
selection
Adapting to clients’ feedback
Recommender System for Investment Ideas

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Challenges in Finance
Model Based Collaborative Filtering: Implicit Ratings
2
1 3
5
4
1
4 2
2

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Challenges in Finance
Model Based Collaborative Filtering: Implicit Ratings

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? ? ? ? ? ?
? ? ? ? ?
? ? ? ? ? ?
? ? ? ? ? ?
? ? ? ? ? ?
? ? ? ? ?
? ? ? ? ? ?
Challenges in Finance
Model Based Collaborative Filtering: Implicit Ratings

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1
1 1
1
1
1
1 1
1
Challenges in Finance
Implicit Ratings as simply Indicator function?

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15
5 8
3
10
1
9 6
2
Challenges in Finance
Implicit Ratings as Holding Period in Months?

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1
0.7 0.1
0.3
0.1
0.8
1 0.9
0.5
Challenges in Finance
Implicit Ratings as Trading activity?

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1
1 1
1
1
1
1 1
1
Challenges in Finance
Usage of Confidence Weights

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1 0 0 0 0 0 0
0 0 1 0 1 0 0
1 0 0 0 0 0 0
0 0 0 1 0 0 0
0 0 0 0 0 1 0
0 1 0 0 0 0 1
0 1 0 0 0 0 0
Challenges in Finance
Usage of Confidence Weights

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1 0 0 0 0 0 0
0 0 1 0 1 0 0
1 0 0 0 0 0 0
0 0 0 1 0 0 0
0 0 0 0 0 1 0
0 1 0 0 0 0 1
0 1 0 0 0 0 0
Challenges in Finance
Usage of Confidence Weights
𝑖,𝑗
𝑐𝑖𝑗 𝑝𝑖𝑗 − 𝑝𝑖𝑗
2

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Collaborative filtering Finding explanations
Product
binning
Asset
selection
Adapting to clients’ feedback
Recommender System for Investment Ideas

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0 1 1 1 0 0 0
0 1 1 1 0 0 0
1 1 1 1 0 0 0
0 1 1 0 1 1 0
0 0 0 1 1 1 1
0 0 0 1 1 1 0
0 1 0 0 0 1 1
Goal: produce recommendation for client 4
Explainability
Find Co-clusters in the Matrix
Reinhard Heckel, Michail Vlachos, Thomas Parnell, Celestine Duenner, Scalable and interpretable product recommendations via overlapping co-clustering, In IEEE International
Conference on Data Engineering (ICDE) 2017

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0 1 1 1 0 0 0
0 1 1 1 0 0 0
1 1 1 1 0 0 0
0 1 1 1 1 0
0 0 0 1 1 1 1
0 0 0 1 1 1 0
0 1 0 0 0 1 1
Recommendation: Item 4 is recommended to Client 4 because:
Client 4 has purchased Items 2-3: clients with similar purchase history (clients 1-3) also bought Item 4
Client 4 has purchased Items 5-6: clients with similar purchase history (clients 5-6) also bought Item 4
Explainability
Find Co-clusters in the Matrix

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Overview of the system
Collaborative filtering Finding explanations
Product
binning
Asset
selection
Adapting to clients’ feedback

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Emmi AG
Abb Ltd
The Swatch Group
Baloise Holding
Swiss Re
Swiss Life Holding
Jelmoli
Orell Fussli
Sensirion Holding
Swiss Equities
CHF
Industry
Risk Level 2
Swiss Equities
CHF
Industry
Risk Level 3
German Equities
EUR
Transportation
Risk Level 3
Swiss Bonds
CHF
Insurance
Risk Level 2
Individual assets
Asset categories
Air Berlin
Deutsche Lufthansa
Deutsche Post
Ratings matrix
1 0 0 1 1 0
0 1 0 0 0 1
1 1 0 0 0 0
0 0 1 0 0 0
0 0 0 1 1 0
client1
client2
client3
client4
...
Product Binning
Approach

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Product Binning
Which Issues it Solves
Cold-start for products
Time-varying product features
Too many dimensions
Allows coupling with business logic
Too much sparsity
Can help with extension of bank’s recommendation lists

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Overview of the system
Collaborative filtering Finding explanations
Product
binning
Asset
selection
Adapting to clients’ feedback

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Swiss Equities
CHF
Industry
Risk Level 2
Swiss Equities
CHF
Industry
Asset categories Individual assets
Emmi AG
Abb Ltd
The Swatch Group
historical holding
historical holding
current holding
Personal filter Recommendation
Category expansion by dropping
‘Risk Level’
historical holding
historical holding
current holding
current holding
Emmi AG
Abb Ltd
The Swatch Group
Jelmoli
Orell Fussli
Sensirion Holding
Orell Fussli
Asset selection from a recommended category
Attempt 1
Attempt 2
Product Binning
Compliant Selection from the Bin

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Outline
3
2
5 Integration into Banking Core Services
1 Introduction to Recommender Systems
Challenges in Finance
Latent Features and Collaborative Filtering
4 Reaction to Explicit Feedback

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Overview of the system
Collaborative filtering Finding explanations
Product
binning
Asset
selection
Adapting to clients’ feedback

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Explicit Feedback
Approach
𝐿 =
𝑖,𝑗
𝑐𝑖𝑗 𝑝𝑖𝑗 − 𝑝𝑖𝑗
2
• 𝑝𝑖𝑗 = 0
• 𝑐𝑖𝑗 = 𝑐 𝑚𝑎𝑥
• 𝑝𝑖𝑗 is forced to be 0
• 𝑝𝑖𝑗 = 𝑝 𝑚𝑎𝑥
• 𝑐𝑖𝑗 = 𝑐 𝑚𝑎𝑥
• 𝑝𝑖𝑗 must be high
Clients can rate the ideas: binary feedback
Feedback enters the prediction loop for the next
batch of recommendations
Experiments showed this approach to be legit

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Outline
3
2
5 Integration into Banking Core Services
1 Introduction to Recommender Systems
Challenges in Finance
Latent Features and Collaborative Filtering
4 Reaction to Explicit Feedback

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Investment Ideas in the Core Banking System

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INVESTMENT IDEAS
Publishing Ideas on the E-Banking Platform

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HOW DO YOU RATE THIS IDEA?
Your feedback helps to improve ideas for you.
Please note:
This is an investment idea and not a personal investment
recommendation (investment advice). It does not take into
account your financial situation or investment objectives.
Please click here to find more information.
As you bought ABB, you may like LafargeHolcim.
IDEA DESCRIPTION
AIRS Learns Based upon Feedback

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Why recommenders for financial advice?
Relevant investment ideas with high acceptance probability
Are achieved by employing the collaborative filtering approach with implicit ratings
Quality control
Is easily integrated through product binning and smart post-filtering
Saves time and effort of Relationship Managers
By automatically providing detailed explanations
Increases usage of online banking system
By presenting the ideas in an appealing way
Stimulates client engagement
Through collecting the feedback and adapting accordingly

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Explicit Feedback
Experiments
“How my feedback affects my recommendations?”
“How feedback of other clients affects
my recommendations?”
clients
number of clients

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Integration into Core Services
Project Timeline
May 18 Jun 18 Jul 18 Aug 18 Sep 18 Oct 18 Nov 18 Dec 18 Jan 19 Feb 19 Mar 19 Apr 19
AIRSPoC(PhaseI)AIRSPROD(PhaseII)
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UAT
DEV
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Evaluation by CRMs
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