Vision 2014: Big Data for Business Lending

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Big Data for business lending

Sharry Ditzler Washington Trust Bank

Connie Miller Washington Trust Bank

#vision2014

Didi Frohardt Experian


Big Data definition

Top-3 business lending strategies

Washington Trust Bank story

Getting started

Agenda


Technical definition

Business lending definition

Big Data definition

The definition of Big Data refers to groups of data that are so large and

unwieldy that regular database management tools have difficulty

capturing, storing, sharing and managing the information “ ” – Yourdictionary.com

Data locked in silos and the lack of common customer identifier that

could link accounts and services “ ” – Wall Street Journal


Loan growth Internal and external

1:



Risk management Relationships and exposure

Regulatory compliance and

“suggestion”

► Stress testing

► Concentration reporting

2:




Efficiency Disparate systems

Redundant data entry

3:


Commercial trends

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

Bil

lio

ns

Commercial and industrial loans


Commercial trends

1,020

1,030

1,040

1,050

1,060

1,070

1,080

1,090

1,100

1,110

1,120

Bil

lio

ns

Commercial real estate loans


Commercial trends

0

100

200

300

400

500

600

Bil

lio

ns

Construction loans


Commercial trends

56.63

65.56

59.68

62.19

70.82

72.79

73

72.26

80.69

87.25

0 20 40 60 80 100

>1T

100 - 999 B

50 - 99 B

5 - 49 B

1 - 4.9 B

500 - 999 M

250 - 499 M

200 - 249 M

50 - 99 M

0 - 49 M

Efficiency ratio (as of 12/31/2013)


Washington Trust Bank

So, how can you

do it in real life? Q:


$4.37 billion in assets (as of 12/31/2013)

112 years old, privately held, commercial bank

Serving the following primary markets:

► Washington, Idaho, Oregon

$3.0 billion total loan portfolio

► 8-9% loan growth FYE2013 – expected again for FYE2014

Washington Trust Bank Spokane, WA

C&I Agriculture CRE/C&D Residential Consumer

$835 M $208 M $1.2 B $611 M $101 M

28% 7% 42% 20% 3%


Core system conversion

► Based on specific bank goals

Disparate systems converted or integrated into new platform

► Third party relationship

● Credit card

● Trust

● Cash management

● Investments

● Online banking

● CDARS

● Corporate contribution system

● Merchant

Washington Trust Bank Disparate systems


Construction loans

Flooring – unit pricing

Many access databases and spreadsheets

► Auditing

► OREO

► SAD

► HLC

► Loan review

► Commercial incentive

► CRA tracking

► Market research – MCIF

Large data repository

Washington Trust Bank Disparate systems


In house data warehouse

► Core to data warehouse balancing in place

Provides a more comprehensive and accurate data source

Data Integration to advisor platform

Everyone uses the same data points

► Consistency in reporting

► Same version of the truth

► Data naming standards/data use standards

Standard reporting package driven from senior management down through organization

Consistently deploy reports from one portal

Reduces custom report requests

Washington Trust Bank Data warehouse


Converted access databases and spreadsheets now in advisor/originations

► Cash management

► Commercial incentive

► OREO

► Special assets

► Home loan center

► Compliance

● CRA

● SBA 504 reporting

► Loan review

► Market research – MCIF

Washington Trust Bank Experian solutions – effected


Data consistency with Jack Henry

Custom report requirements

► Data at user fingertips – data queries on the fly

► Scheduled reports for historical – retention purposes

Relationship groups

► SBLF reporting

► Relationship and exposure reporting



Complete picture of client from prospect to special assets/payoff

Portfolio risk management

► Complete picture of borrower when trigger fires

► Run triggers across the bank portfolio – all consumer loans

► Small business loans



Continuing to bring data together in one place…

2014 and beyond

► Balance advisor platform to data warehouse

► Advisor data to data warehouse – for data not in Jack Henry

► Credit memo – with analysis – ability to pull key points out of credit memo

● FTE

● Revenue

► Stress testing data

► Appraisal data

► Pricing – current in house developed system

► ALLL

Washington Trust Bank What is left to do?


The results!


The results…

59.02

64.57

65.84

75.48

63.82

67.58 68.1 71.12

50

55

60

65

70

75

80

2011Q1

2011Q2

2011Q3

2011Q4

2012Q1

2012Q2

2012Q3

2012Q4

2013Q1

2013Q2

2013Q3

2013Q4

Efficiency ratio

Washington Trust Bank Peers


The results…

2.65 2.72 2.71 2.64 2.68 2.84 2.9 2.9 2.94

3.08 3.16 3.18

0

0.5

1

1.5

2

2.5

3

3.5

11Q1 11Q2 11Q3 11Q4 12Q1 12Q2 12Q3 12Q4 13Q1 13Q2 13Q3 13Q4

Total outstanding loans


The results…

79.2

56.7 54.2

56.8

43.5 39

35.8 39.3

36.6 33.8 34.5

31.6

0

10

20

30

40

50

60

70

80

90

11Q1 11Q2 11Q3 11Q4 12Q1 12Q2 12Q3 12Q4 13Q1 13Q2 13Q3 13Q4

Total non-performing loans


Strategies

Determine future state/vision

► Senior management buy-in

Disparate system data review

► Diagram/inventory of architecture of all systems/applications

● Understanding where data is residing and interfacing with key data stores

► Consolidate and/or eliminate systems/spreadsheets/databases that aren’t relevant

Getting started!


Strategies

Prioritize data required to support business processes/vision

► Determine the data repository to hold the data

Integrate information

► All customer data in one system

Q&A

Getting started!


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Vision 2014: Big Data for Business Lending

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