EVIDENCE-BASED RISK MANAGEMENT
D&O and Cyber Threats
Casualty Risk
SRMC Benchmarking
ANDREW D. BANASIEWICZ, PH.D.
Drowning in Data and Still Guessing…
Copyright Andrew D Banasiewicz. All rights reserved.
2
A Word About Myself: A Combination of Practice & Research
 Two decades of industry, research & predictive
analytical experience
 Four books, numerous articles and white papers
 Numerous domestic and international conference
presentations & keynotes, including RIMS and Global
Risk Forum
Copyright Andrew D Banasiewicz. All rights reserved.
3
Erudite Analytics Offerings
Executive
Risk
Structured Solutions
• Owned event datasets
• Proprietary algorithms
D&O | Cyber
Casualty
Risk
WC | GL
Evidence
Based Risk
Management
Ad Hoc
Solutions
Explore | Predict
Custom Analyses
• Benchmarking
• On-demand studies
Copyright Andrew D Banasiewicz. All rights reserved.
4
Structured Solutions
D&O | CYBER THREATS
5
Conceptual Backdrop: The Meta Model of Executive Risk
Copyright Andrew D Banasiewicz. All rights reserved.
6
Focusing on Securities Class Actions (SCA):
Data & Analyses
Input: Cause
Key Events: IPO;
M&A
Restatements
Accounting Accruals
Key Ratios from
Balance Sheet &
Income Statement
Downside Stock
Price Volatility
Firmographics
Input: Outcome
End Result
Data
Amalgamation,
Processing &
Analyses
Company-Specific
Likelihood &
Severity Estimates
SCA Filings &
Settlements
Copyright Andrew D Banasiewicz. All rights reserved.
7
SCA Likelihood Estimation
Input data sample:
Confusion Matrix: Actual vs. Predicted
• 441 verified public co SCA filings (last 3 years);
• A sample of 912 of non-SCA public companies;
• All available firmographic, financial & stock
movement data
Predicted SCA Filings
No
No
Actual SCA
Filings
Total
Count
792
120
912
%
87%
13%
100%
Count
210
231
441
Yes
Total
Yes
%
48%
52%
100%
Count
1,002
351
1,353
%
74%
26%
100%
SCA
Overall avg.
No SCA
Regression Trees
Copyright Andrew D Banasiewicz. All rights reserved.
8
SCA Likelihood Scoring: All Public US Companies
Each public US company was scored with the earlier-derived several hundred regression trees,
the results of which were averaged to arrive at company-specific SCA likelihood, leading to the
aggregate distribution (left) and classificatory typology (right).
N
Mean
Median
Std. Deviation
Minimum
Maximum
10
20
25
30
40
50
60
70
75
80
90
95
99
Percentiles
Aggregate SCA Score Distribution
Mean = .304
Std. Dev. = .186
N = 11,019
11,019
0.304
0.283
0.186
0.005
0.913
0.086
0.130
0.146
0.172
0.228
0.283
0.327
0.385
0.422
0.459
0.563
0.665
0.816
Classificatory
Typology
Low
Moderate
Elevated
High
Objective assessment of their own unique exposure enables companies to make more rational
risk transfer (i.e., D&O coverage) choices – for instance, Low SCA likelihood suggests higher SIR
Copyright Andrew D Banasiewicz. All rights reserved.
9
SCA Severity Estimation – Multi-Attribute Indexing
Key Highlights:
Estimated Loss Ranges for US Exchanges
Traded Companies
• Based on hand-collected sample of 460
individual, verified SCA settlements;
• Explicitly takes into accounts industry
sector differences;
• Explicitly takes into account regulatory
(PSLRA of ‘96; SOX) and judicial (US
Supreme Court) acts and rulings;
• Adjusts for market cap differences;
• Nets out the ‘expected’ levels of stock’s
downside variability (loss causation);
• Non-linear market cap vs. exposure
estimation (e.g., 3x market cap does not
automatically mean 3x higher loss);
Metric
No. Companies
Mean
Median
10
20
25
30
40
50
60
70
75
80
90
95
Percentiles
• Company-specific estimation;
Expected Loss: Low Expected Loss: High
7,972
$87,211,511
$5,682,518
$200,193
$673,016
$999,476
$1,421,357
$2,752,702
$5,682,518
$11,891,924
$25,434,429
$36,417,244
$55,620,211
$153,910,910
$348,942,557
7,972
$147,920,751
$10,096,950
$447,818
$1,222,868
$1,775,219
$2,457,549
$4,746,858
$10,096,950
$20,621,626
$43,243,010
$61,833,249
$96,342,381
$260,261,239
$581,832,316
• Expected loss expressed as pseudo
credible intervals;
Copyright Andrew D Banasiewicz. All rights reserved.
10
Company-Specific Exposure Reports: Focusing on What Matters
Copyright Andrew D Banasiewicz. All rights reserved.
11
Structured Solutions
CASUALTY RISKS: WC | GL
12
Meeting Obligations in Fiscally Sound Manner
• Typically, casualty claim management is highly reactive, as claim handlers
take action in response to claim development
• The available data makes it possible for claim handlers to act proactively,
which will result in lower claim costs
• Organizations have legal duties when
casualty accidents – WC/GL – arise,
but
• organizations also need to manage
their expenses responsibly.
• Making sound use of the available
data, organizations can accomplish
both objectives.
Copyright Andrew D Banasiewicz. All rights reserved.
13
Managing the Total Cost of Casualty Risk
Claim Management
Loss
Prevention
Incidence Analytics
Risk
Financing
Newly-Open Claims
Residual Claims
(1 year or less since opening)
(1+ years since opening)
Which individual claims are
likely to develop adversely?
How are aggregate claim
carrying costs trending?
What factors inflate shortterm claim costs?
What factors inflate longterm claim costs?
Predictive Claim Scoring
Claim Carrying Cost Index
Financial Analytics
Period Scorecard & Cumulative Knowledge Base
Copyright Andrew D Banasiewicz. All rights reserved.
14
Predictive Claim Scoring
--For Illustrative Purposes Only-An analytic method for estimating the expected future cost of
individual workers comp claims, and for identifying the most
promising risk reduction actions and strategies.
Copyright Andrew D Banasiewicz. All rights reserved.
15
Claim Carrying Cost Index (C3I)
C3I enables claim managers to make meaningful cross-time assessment of cost
development trends and root causes of the ever-changing (i.e., hard to compare)
mix of longer duration (1 year+ since inception) claims.
Copyright Andrew D Banasiewicz. All rights reserved.
16
Ad Hoc Solutions
SRMC BENCHMARKING
17
Imagine If as SRMC Member You Could…
 Benchmark your clients’ coverage adequacy in terms of:
Cost / Unit of Coverage
Limit / Unit of Asset Base
Key Endorsements
High
Endorsement A

Avg.
Endorsement B
Endorsement C
Endorsement n
Low
While also keeping track of changes over time…
 Benchmark your clients’ WC loss experience in terms of:
WC Cost / Revenue
High
WC Cost Over Time
$$
❓
Other
Ideas
Avg.
$
Low
T1
T2
T3
Tn
Copyright Andrew D Banasiewicz. All rights reserved.
18
How Would It Work?
1. We would, jointly, develop a standard data submission form
2. Once finalized, the form would be ‘translated’ into an electronic data capture
format (e.g., Survey Monkey)
a. The basic organizational structure of data would be Client_ID and Year; all other
variables would be treated as attributes
b. There is usually a fairly small ($300/year for SurveyMonkey) cost for data capture
services – that cost would be covered by SRMC
3. Once the electronic data submission system was operational, you would
complete the data capture form for each one of your clients
a. optionally, we could consider going up to 5 years back for ’instant’ longitudinal
tracking
4. I would build a searchable database, develop agreed upon benchmarking
metrics, and maintain it on ongoing basis
5. There would be no charge to SRMC members for basic benchmarking (to be
defined); there would be a charge for any ‘deeper dive’ / nonstandard work
[email protected]
www.eruditeanalytics.com
19