Recommender Systems for Mass Customization of Financial Advice · Recommender Systems for Mass Customization of Financial Advice Private Banking: personalized recommendations of financial

BRANDSCHENKESTRASSE 41

CH-8002 ZURICH

SWITZERLAND

[email protected]

INCUBEGROUP.COM

Recommender Systemsfor Mass Customizationof Financial Advice

SDS|2018, Bern, 07.06.2018

Anna Nowakowska

Head of Data Analytics

[email protected]

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Data Analytics Team at InCube

Recommender Systems for Financial Advice

Private Banking Use Case

4 Retail Banking Use Case

5 Outlook for the Future and Summary

AgendaTalk Outline

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About InCubeOur Focus

We develop fintech products. We provide consulting services.

Full stack product development and expertise-driven consulting

Products and Services for Financial Institutions

Wealth

Tech

Full stack solutions for digital

wealth management driven by

algorithms, artificial intelligence

and user experience

Data

Science

Artificial intelligence for real-word

use cases at financial institutions –

from rapid prototyping to scalable

production systems

Quantitative

Finance

Robotic process automation and

digitization require scalable and

robust quant solutions for

investment, risk and compliance

management

Business

Consulting

Consulting services from project

management, business analysis

and transformation, subject matter

expertise to implementation

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Data

analytics

Software

Engineering

Business

Consulting

Quantitative

Finance

Backend

Engineering

Frontend

Web Apps

Regulatory

Compliance

Business

Analysis

& PM

Machine Learning

Reporting &

Visualization

Predictive

Analytics

Architecture

& Design

UX & UI

Design

InCube Services Data Analytics Team

Data Analytics Team at InCubeIntroduction

Machine

Learning

Anna NowakowskaMEng Electronics & Electrical Engineering, CFA

Head of Data Analytics

Coordinating the recommender system projects

Milica PetrovićMSc ETH Statistics

Data Scientist

Feature-based recommender for retail banking products

Aleksandra ChirkinaMSc ETH Statistics

Data Scientist

Feature-based recommender for retail banking products

Jeremy SeowETH Statistics Master’s Student

Data Science Intern

Advanced collaborative filtering recommender for private

banking products

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Recommender Systems at InCubeTrack Record

Recommender Systems

2016-2017

Stock Recommendation Application

▪ Stock recommendations based on factor selection and weighting

▪ Factor selections can be self-configured or learned by data

H1 2017

Case-Based Reasoning Techniques

H2 2017

Collaborative Filtering Techniques

Q1 2018

Model Fitting to Data of Pilot Bank

Q2 2018 -

Model Extensions with Our Clients

▪ Portfolio proposals based on case base library

▪ Client similarity measures, portfolio retrieval and ranking algorithms tested

▪ Started to cooperate with ETH Zurich on recommender systems

▪ Fitted and tested various collaborative filtering (CF) and boosting techniques

▪ Model- and neighbourhood-based CF models with Nidwaldner Kantonalbank

▪ Working on use cases for retail and private banking

▪ Started projects with two large banks in CH for an advisory recommender

▪ Combine (deep) autoencoders with CF to learn feature representations for

latent factor models in CF

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Optimization engine Optimal portfolio

OutputsOptimizationInputs

Assets

Risk profile

Constraints

Objectives

▪ Many portfolio management processes incl. robo-advisors are based on Modern Portfolio Theory (MPT)

▪ These tools maximize the client’s utility function based on a client risk profile and an applicable offering

▪ However, client’s affinity to the proposed portfolio and products is usually not considered

Recommender Systems for Financial AdviceMaximizing the Client’s Utility Function

Cumulated returns of different securities Efficient FrontierCumulated returns of different securities Weights

Target Risk[Cov]

Valu

e

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Business Value

▪ Maximize client’s utility function and at the same time

the acceptance rate of proposals

▪ New opportunities and reduced manual work for the

wealth managers for HNWI and UHNWI client

segments

▪ Automated but customized advice for retail client

segments

Recommender Systems for Financial AdviceMaximizing the Acceptance Rate of Proposals

Problems

▪ Huge amount of data

▪ 50 to 200 clients per relationship manager

▪ Hard to manage manually

Solution

▪ Similar clients – similar financial needs

▪ Many financial products share similar characteristics

▪ Recommender systems:

➔ propose products used by similar clients

➔ propose similar but unseen products to clients

➔ improve and scale-out customized financial advice

"People who bought this also liked…"

"If you bought this, you might also like…"

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Private Banking Use CaseNature of the Use Case and Data Description

Subset of data from Nidwaldner Kantonalbank

✓ provided by the Nidwaldner Kantonalbank (NKB) and

anonymised for the purpose of this presentation

✓ 1117 users, 1788 items

✓ ~18 products per person (average)

Many offered products (thousands; financial instruments such as stocks, bonds, derivative instruments, etc)

Clients typically own many products

Clients buy and sell investments - substantial history of user-product “ratings”

Goal: personalized recommendations of financial instruments, such as stocks and bonds

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Private Banking Use CaseNKB Data Overview: Ratings Matrix

• Two groups of clients visible

• 1st group: simple behavior

• 2nd group: complex behavior

• |2nd group| > |1st group|

• Complex data structure

→ we expect a popular model to not

be good enough here

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Private Banking Use CaseNKB Data Overview: How Many Products per Client?

Number of products held by client

% o

f cl

ien

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Clients hold more than a few products

Average number of 18 products per client, which is a good

basis for using collaborative filtering models

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Private Banking Use CaseCollaborative Filtering Approach

Model-based collaborative filtering (CF)

+ no user or item features required

+ typically more accurate than other models

- cold start

- difficult to interpret

→useful for private

banking use case

(abundant history)

Challenges

missing data points

→ client didn’t want the product? OR doesn’t know about it?

implicit ratings

→ how to determine if the clients liked the products they bought?

Modeling approach

1. Ratings matrix factorization to discover latent features

2. Confidence weights – fix confidence weights in one model

3. Boosting – confidence weights estimated from the ensemble model

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Private Banking Use CaseResults

RESULTS

Model-based CF Popular Model

AUC 0.9077 ± 0.0003 0.8728

nDCG 0.3586 ± 0.0004 0.2656

Model based CF wins against the popular model

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Retail Banking Recommender DemoQlik Sense App: Top 5 Account Recommendations per Client

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Retail Banking Recommender DemoQlik Sense App: Account Recommendations Across Client Segments

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Retail Banking Use CaseNature of the Use Case and Data Description

Data from Nidwaldner Kantonalbank

✓ provided by the Nidwaldner Kantonalbank (NKB) and

anonymised for the purpose of this presentation

✓ ~100 available products

✓ Product categories:

• Current accounts

• Savings accounts

• Credit card accounts

• Investment accounts

• Mortgages and loans

• etc.

Relatively few offered products (tens to hundreds; current or savings accounts, credit cards, etc.)

Clients typically own few products

Clients rarely change products

Goal: personalized recommendations of retail banking products, such as accounts and credit cards

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Retail Banking Use CaseData Overview: Top 20 Most Popular Products and % of Clients Holding Them

pro

du

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% of clients holding the product

A few very popular products

A few products popular across the client base,

the rest only held by smaller groups of clients

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Retail Banking Use CaseData Overview: How Many Products per Client?

Most clients only hold a few accounts

Average number of 2.9 products per client, over the entire

history of the client being with the bank

% o

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Number of products held by client2.9

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Retail Banking Use CaseWhy Model Based Collaborative Filtering Fails & Our Model Choice

RESULTS

Model-based CF Popular Model

Accuracy 8.96 ± 0.18% 20.43 ± 0.23%

Mean Reciprocal Rank 17.72 ± 0.18% 31.53 ± 0.22%

“Popular” (non-personalised) model performs better than model-based CF

• clients consume too few products

• low variety of the most popular products

Memory-based demographic collaborative filtering

+ cold-start solved through user features

+ easily interpretable: explanations for recommendations

- requires collection of features

- need to store full matrix

→ useful for retail banking use case (limited history)

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Retail Banking Use CaseMemory Based Demographic Collaborative Filtering: Approach

Step 1: Demographic segmentation

For each client, find a neighbourhood of k similar clients (k-NN)

based on the Gower distance and features:

• gender

• age group

• wealth group

• e-banking usage

• 3rd pillar payments

• ...

Step 2: Product popularity

Within each client’s neighbourhood:

• determine the popularity of each product - how many clients in the

neighbourhood consumed it

Step 3: Personalized recommendation

For each client:

• sort the products the client has not yet consumed by popularity in

his / her neighborhood

• recommend the top 5 products

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Retail Banking Use CaseResults

RESULTS

Model-Based CF Popular Model Memory-Based Demographic CF

Accuracy 8.96 ± 0.18% 20.43 ± 0.23% 45.11 ± 1.27%

Mean Reciprocal Rank 17.72 ± 0.18% 31.53 ± 0.22% 58.44 ± 1.01%

Memory based demographic CF wins against popular model and model-based CF

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Outlook for the futureRecommender Systems for Mass Customization of Financial Advice

Ongoing projects

✓ Working with two large Swiss banks on an advisory

recommender system in private banking

Modeling and evaluation approach

✓ Online evaluation → explicit feedback

✓ A/B testing

✓ Features changing in time

✓ Hybrid models

✓ Incorporating both item and client features

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SummaryRecommender Systems for Mass Customization of Financial Advice

Private Banking: personalized recommendations of financial instruments

• Many products, diversity of investing behaviour across the client base, abundant history of ratings per user

• Model-based Collaborative Filtering

• higher confidence weights for non-missing data points

• start with a simple matrix decomposition model with fixed confidence weights

• apply boosting with a number of component models, between which confidence levels are adjusted

Retail Banking: personalized recommendations of accounts, credit cards, mortgages, etc.

• Few products per user, limited history of ratings

• similarities between users calculated using the Gower distance

• for each user, his “neighbourhood” of 150 most similar users is determined

• the most popular products in this neighbourhood are recommended to this user

• Not suitable for ratings matrix decomposition

• Memory-based Demographic Collaborative Filtering

BRANDSCHENKESTRASSE 41

CH-8002 ZURICH

SWITZERLAND

[email protected]

INCUBEGROUP.COM

Thank you!

Anna Nowakowska

[email protected]

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Recommender Systems for Mass Customization of Financial Advice · Recommender Systems for Mass Customization of Financial Advice Private Banking: personalized recommendations of financial