how data analytics can help risk management

How data analytics can help with risk management

Darren James, Partner, Deloitte

Thursday, February 24, 2011

Business Risk Management Seminar Series

2010/2011 Toronto sessions

© Deloitte & Touche LLP and affiliated entities.

Discussion items

• Data analytics in the context of risk management

• Background: What is data analytics

• Applying data analytics to risk management

• Case studies

• Closing thoughts

Business Risk Management Seminar Series 2010-20111


Data analytics in the context of risk management



Opportunity for using data analytics in managing risk

• Explosive data growth means more raw materials

• Innovation in data generation and capture

• Data supports fact-based decision making

• Already used extensively in many areas of business

• Data analytics focusing on risk are primarily used in the areas of credit

risk, anti-money laundering and fraud


Data analytics has significant potential to be exploited in the

risk management space


What will the future hold

• Will boards be asking us to back up our gut feel on risk with hard data?

• Will the C-Suite want to understand the key risk factors and their relative

importance in real numbers?

• Will management have even greater responsibility to foresee future

risks long before they manifest themselves?

• Will data analytics be a core competency for all risk professionals?


Data analytics is a business tool that will be pervasive in our

organizations


Background: What is data analytics



What is data analytics

“A practical definition, however, would be that analytics is the process of

obtaining an optimal or realistic decision based on existing data.”

(Wikipedia)

“Data analytics is the science of examining raw data with the purpose of

drawing conclusions about that information.”

(whatis.com)

“Analytics leverage data in a particular functional process (or application) to

enable context-specific insight that is actionable.”

(Gartner)


Data analytics is the use of raw data to produce insights or

conclusions that can be acted upon


How can we categorize data analysis methods


Descriptive Statistics

Exploratory Data Analysis (EDA)

Confirmatory Data Analysis (CDA)

Rules-based

(Human Intelligence)

Supervised Learning

Unsupervised Learning

Inference-based

(Machine Learning)


What types of questions can analytics answer


Future Perspective

What if these trends

continue?

What will happen next?

What’s the best that can happen?

Current Perspective

Where is the problem?

What actions are needed?

Why is this happening?

Historical Perspective

What happened?

How many, how often, where?


Some sample data analytics techniques are...

• Clustering

• Predictive Analytics

• Association Rule Learning

• Regression Analysis

• Visualization

• Decision Tree Learning



How does data analytics apply to risk management



How can data analytics be applied to risk management


Historical perspective

• Error detection and quantification – Targeted analytic applications to detect errors (e.g., business unit reviews or internal audits)

Current monitoring

• Risk Dashboard/Continuous Monitoring – How are we currently doing? What is our current risk profile?

Forward-looking

• Key Risk Indicators (KRIs)

• “What-if” – How will this decision affect our risk?


Some examples of historical-type questions...


• How many stock-outs did we have?

• Which stores were they in?

• What caused them?

• What could have prevented them?

Historical perspective • Error detection and quantification


Some examples of things we might monitor...


Current monitoring • Risk Dashboard/Continuous Monitoring

• How are our stock-outs trending?

• Where do we continue to have problems?

• Where are inventory controls failing?

• What is our current opportunity cost from empty shelves?


Some examples of proactive questions...


Forward-looking • “What-if” and What will happen next?

• Where will the next stock-out occur?

• What if we increase our minimum holding levels?

• What changes do we need to make to reduce the number

of stock-outs?

• What are our optimum stock levels to balance the risk of

stock outs with holding costs?


Data analytics can enhance our existing KRIs


• Develop more sophisticated multi-dimensional KRIs

• Identify KRIs that more closely correlate with desired

outcomes

• More accurately determine the contribution of a given

indicator to overall risk

• Provide a more fulsome picture of risk profiles by

monitoring and trending a more comprehensive range of

indicators

Forward-looking • KRIs


Data analytics can enhance our early warning capabilities


• Components of an early warning system

‒ Sensors to collect data

‒ Systems to accumulate and process the data

‒ Analytics to provide insights from the data

‒ Something/someone to interpret the results

‒ Something/someone to action the interpretation

Forward-looking

• Early warning systems


Increasing range of sensors creating raw data

Raw Data

Business Systems

Network Systems

Operations

Systems

Security Systems

Surveys

Phone Calls

Email

Video

Business Partners

Social Media

Customer

Data Vendors



An increasing variety of innovative sensors are becoming available



Innovators are developing new sensor deployments


The Quake-Catcher Network is a

collaborative initiative for

developing the world's largest, low-

cost strong-motion seismic network

by utilizing sensors in and attached

to internet-connected computers.


Generating good quality data from sensors

• Text mining

• Audio analysis

• Video analysis

• Entity resolution

• Data enhancement

– E.g. Census data, postal data



Computers are being developed to handle Big Data

• Big Data is a term used for data sets that are too large for existing

standard software to be able to process within a workable time frame

• New computing systems have needed to be developed to handle Big

Data including massively parallel processing (MPP) databases, cloud

computing platforms and data mining grids



Case studies



Large retail bank• Objective:

• Identify high risk branch locations for branch

audit visits

Self Organizing Maps were used to analyse all relevant

data from the client’s national branch network. This

allowed analysis of a broader array of key risk

indicators than usual under traditional approaches

• Data Analysed:

• Financial – P&L, delinquency, compliance,

average holding size, credit quality, portfolio

risk

• Branch Staff Data – turnover, bonus payments,

leave balances, trends and staff demographics

• Customer/Sales Data – number of products per

customer, accounts opened/closed, source of

new customers, account profitability

• Other – suspense account activity, audit

findings, fraud incidents

• Output highlighted outliers within the branch network

and allowed for a purely risk-based branch audit

selection approach

23

Analysis of branch data highlight s behavioural outliers, and helps

direct audit activity. From this analysis, branch 122 had

exceptional characteristics relating to a combination of: higher than

average no. of loans; higher than average loan value ($); large no.

of loan defaults combined with 5 other above average parameters

Further analysis highlighted activity by quarter in relation to

opening new accounts. We can direct audit effort to investigate

into what is driving this behaviour

Bank branch network analysis for audit selection


Type A - Number of events Type B - Number of events

Large resources companyTraditional safety analytics defined scale of the safety

problem, but lacked insight to why events occurred.

Using a strategic safety profiling analysis we:

• Objectively identified the key factors and behaviors

that impacted safety related incidents and then

designed measurable interventions to minimise

safety risk

• Used the profiling model to predict the most likely

next person(s) at risk to get hurt

Data Analysed:

• Permanent records, Payslips, Leave history

• Rosters (including FIFO), training history / results

• Performance reviews

• Access card history

• Injuries sustained / near misses / hazards

• Severity of injuries

• Equipment involved

• Location of event

• Weather observations at time of event

24

Type A staff are almost eight times more likely to have

suffered a safety event. Impact is 240% more severe than

average, exclusively male, 20% older than average, unionised

and residential at the mine site. Tend to get hurt in the

beginning of their roster (1st or the 2nd day), through an object

causing them harm and have not completed a required safety

training unit.

Type B staff are six times more likely to have suffered a safety

event with an impact almost 300% more severe than average.

Their accidents are expensive tending to be sprains or soft

tissue damage. In contrast to type A, these employees

generally get hurt on the 7th day of a 7 day roster – just before

they roll off.

Safety analytics diagnostic


Closing thoughts



Closing thoughts

• Data analytics requires innovative thinking about sourcing data and

identifying sensors

• Data analytics is as much, if not more, about asking the right questions

as it is about the mathematical contortions going on behind the scenes

• Data analytics can be applied to more aspects of risk management than

just credit risk, AML and fraud



For more information

If you would like more information about Data Analytics or how Deloitte

can help your organization, please contact:


Darren James

Partner

Enterprise Risk

416-601-6567

[email protected]

mailto:[email protected]


Next Business Risk Management Series session

Session 7:

Internal audit – Ensuring strategic relevance

Date: Thursday, March 10, 2011

Venue: Toronto Board of Trade

RSVP: [email protected]


how data analytics can help risk management

Business

risk professionals

risk of stock outs

risk management case

deloitte touche llp

affiliated entities

current risk profile

data generation

areas of credit risk