AI IN RISK MANAGEMENT &RISK MANAGEMENT IN AI

ETH RISK DAY 2018, SEPTEMBER 14

DR. ISABELLE FLÜCKIGER

RISK MANAGEMENT IN AI,

AI IN RISK MANAGEMENT,

WHERE THE JOURNEY HAS STARTED,

ONCE UPON A TIME AT THE ETH

Where the journey has started

2.4.97

INNOVATIONS IN RISK MANAGEMENT

Where the journey has started

AI IN RISK MANAGEMENT

INTERACTION OF BUZZWORD TRENDS

AI in Risk Management

ROBOTIC PROCESS AUTOMATION

NEURAL NETWORKS

MACHINE LEARNING

ARTIFICIAL INTELLIGENCE

Artificial IntelligenceThe theory and development of computer systems which take over tasks normally requiring human intelligence; a term covering e.g. machine and deep learning.

Machine Learning & Statistical Modelling Has always played a significant role in risk management. With the evolvement of new technologies, the amount of potential use cases will significantly increase.

Neural Networks

One of many methods of ML whichmanages to get by on littleprerequisites, requires, however, a significantly larger amount oftraining data to learn

Robotic Process Automation The science of automating routine business operations with "software robots" in order to perform tasks automatically.

Intelligent Automation The combination of AI and RPA, has an actual “understanding” of business processes and their variations, and takes this knowledge into account when executing automated business process.

DEEP LEARNING Deep Learning

Class of algorithms for specific kinds of NNMultilayered, «deep» neural networkcomprised of at least three layers NNs.

Big DataThe industry is no longer talking about “big data” but rather just “data”. It is everywhere and it is essential for companies to be able to handle it.

NEW TECHNOLOGY APPLICATION IN RISK MANAGEMENT

AI in Risk Management

Risk Management• Risk Planning• Risk Analytics• Risk Monitoring• Risk Scoring• Counterparty Risk• Stress Testing

Identity Management• Identity Verification • Client Screening (KYC, OFAC) • Regulatory Onboarding • Data Management • Customer Due Diligence

Transaction Management• Transaction Monitoring• Liquidity Management• Auditing• Post Trade Control Solutions• Anti-fraud Solutions• AML Screening• Commission Management

Technology Applications

Technology Applications

Technology Applications

Technology Applications

Value DriversLower cost of

compliance

Accelerated Time to Market

Enhanced Governance

Scalable, Integrated Solutions

1

AI/ Machine Learning

1 Big Data Analytics

2 Automation/ Robotics

3 Blockchain4 Cloud5Technology Applications

Biometrics6

Enhanced User Experience

Regulatory Reporting• Build Real-time Reports• Distributing Regulatory Reports• Data Management• Pre and Post Trade Reporting• Treasury and EMIR Reporting

1

1

1

3

2 2

2

3

42 6

5

55

3 5

Lower Risk

Competitive Advantage

64 4

4

EXAMPLE 1 – INTELLIGENT REPORT COMPARISON

AI in Risk Management

Dashboard

EXAMPLE 2 - ONLINE RISK MONITORING SOLUTION

AI in Risk Management

Underwriters use the dashboard to

monitor and research risk to determine

underwriting, renewal, pricing decisions

and to discuss with the companies

Renewal

Pricing

Underwriting

ORMS collects the data from hundred of

thousands of websites and adds Artificial

Intelligence on top of the data to provide

curated underwriting relevant information

Internet content provides valuable signals for

emerging risks on commercial entities

(schools, pharma companies etc.). However,

collecting and analyzing information manually

is not scalable

Content Artificial Intelligence

Social

Forums

Lawyer Websites

News

Other

cnn.com

e.g. school websites

Facebook

Yourlawyer.com

Parenting/, education

forums

Content Collection

Topic Modeler

Underwriting Relevance Scoring

Collection QueriesTopic

Aggregation

EXAMPLE 3 – CRISIS EARLY WARNING

AI in Risk Management

Verbal Conflicts (based on GDELT & CAMEO) provide an early warning of

political (and economical) crisis

Advanced Machine Learning

classification models are used to

ensure high forecasting accuracy

Design Thinking Approach to create

user centric UI based on state of the

Art visualizations tools such as

Tableau / QlikSense can be used

CRISIS EARLY WARNING SYSTEM (CEWS)

ENHANCED RISK RESILIENCE & ADEQUATE

CRISIS RESPONSE

Crisis MonitoringCrisis Assessment

• Collection of quality controlled crisis data

• Analysis of crisis triggers & indicators

• Ongoing update of database

• Monitoring indicators of crisis

• Develop forecasting models of crisis

• Ongoing training of underlying ML models

Clear understandable information on emerging crisis for decision makers

Generate robust and accurate forecast of predefined crisis

Integrate connection to financial risk management of banks or corporates

Recognition of risks before it materializes e.g. 3- 9 months in advance

The solution can be easily deployed to on-

premises infrastructure as well as to any

private or a public cloud provider

Use publicly available sources, cloud plattform, machine learning and

state of the art visualization tools to forecast and manage crisis

GDELT: Global Database of Events, Language, and ToneCAMEO: Conflict and Mediation Event Observations

RISK MANAGEMENT IN AI

INTRODUCTION & CHALLENGES

Risk Management in AI

EROSION OF TRUST

Risk Management in AI

IMMEDIATE IMPACTFUL INVISIBLE

COMMON ETHICAL ISSUES RAISED BY AI

Risk Management in AI

JOB APOCALYPSEAI is so ruthlessly efficient that it will lead to massive job loss.

INCLUSION & DIVERSITYThe power of AI is in the hands of the few and with the traditional power brokers.

ARTIFICIAL STUPIDITYAI is actually pretty stupid right now, and can lead to an embarrassing PR incident or worse, do something discriminatory.

THE SINGULARITYWe will create something that is more intelligent than humans and we will lose control.

LACK OF TRANSPARENCYTrue (scary) AI doesn’t explain itself.

PRIVACYAI will erode our notions of data privacy and do things with data that we didn’t consent to.

ETHICALISSUES

EXPECTATIONS TOWARDS AI

Risk Management in AI

Explainable AIUnderstandable

AI

Fair AIEthical AI

Responsible AI

RESPONSIBLE AI (SAMPLE)

Risk Management in AI

OperateDiscover Develop

Model Bias Operational BiasData Bias

Controls

AI Enablers

Responsible AI

Training

Data Science Mentor

ML Playbook

Model Monitoring

Ethical Use

Comprehensive Data & Algorithm Impact

Assessment (CDAIA)

Algorithmic Impact Assessment (AIA)

Data Impact Assessment (DIA)

Privacy Impact Assessment (PIA)

17

EXAMPLE - MODEL LIFECYCLE CONTROLS (EXTRACT)

Model Development

Discover Develop Operate

Identify business problem Model Deployment

Assign DS Coach Comprehensive Data & Algorithm Impact Assessment (CDAIA) Model Monitoring

Functional

Group

• Work with AI Accelerator to define approach, complete PIA and build value case.

Privacy Impact Assessment Data Impact AssessmentAlgorithmic Impact

Assessment• Perform model evaluation• Review outcomes for bias

• Evaluate project’s ability to meet CDAIA criteria with support from DS Coach

• Complete DIA for applicable use cases

• Identify areas of potential data bias

• Complete assessment on model bias

• Outline explainability of model

Supervisor

• Review & approve PIA• Accept use of data

• Review DIA• Assess ethical risks

associated with data use

• Assess model risk• Provide acceptance for

the deployment to production

Accelerator

• Assign the DS Coachbased on project type and availability of Coaches

• Support Pod team to answer PIA

• Review DIA to identify bias

• Obtain DS Coachanalysis

• Escalate high risk models

Model ManagementExperimentation within LabData Acquisition

Risk Management in AI

Copyright © 2018 Accenture. All rights reserved. 18

Data sources

Data collection Data

processing Ideation

Disparate

impact

Analysis Evaluation

Data

Scientist

Scale and implement

Test

Planning

Product Monitoring

Comparison

across different

models

Champion

challenger

EXAMPLE - INTEGRATING FAIRNESS ASSESSMENTS IN THE DATA SCIENCE WORKFLOW

Design thinking

workshop

Algorithmic fairness based on the definition of fairness as equal impact across groups.

Algorithmic Fairness Assessment

Equalised Odds,

Equalised FPR

Risk Management in AI

Thank you.