Moody's Analytics Webinar: New Generation of Credit Decisioning - Next Generation Capability - Moody's Analytics
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Moody’s Analytics Webinar: New Generation
of Credit Decisioning
Next Generation Capability
22 May 2018
Agenda
Nelson Almeida – CreditLens™
Jamie Stark – Alternative Data in Credit
Scoring and Decisioning
2
Pain Points Cited by Banks in Lending
Front-office Middle-office Back-office
Credit Decisioning Monitoring &
Loan Information Documentation & Servicing &
Credit Assessment and Loan Portfolio
Process Gathering Booking Collections
Structuring Management
• Manual data • Manual and • Lack of • Inability to store • Inability to • Lack of early
entry: duplication excel-based automated credit and archive automatically warning signs for
of data entry and calculations for approval process documents generate letters covenant
errors spreading, and notifications breaches and
scoring or pricing • Inability to • Inability to track to customers defaults
• Inability to auto- accurately assess and produce
import financial • Lack of a scoring exposures documents for • Lack of a risk and
data directly from model that legal and reporting
3rd party sources reflects the • Lack of pricing compliance dashboard
model purposes
business factors
• Lack of sufficient
data on • Lack of dual risk- • Manual data entry
borrowers/low ratings of information in
quality data credit
• Inability to presentation
• Slow availability benchmark template
of borrower data against industry
peers
• Lack of an automated business rules engine with integrated workflow functionality
3
The Challenges with Legacy Systems
and Processes
Characteristic Impact Bottom Line
Manual and Paper
Based Processes
Limited Coordination of Slow “Time to Decision” and
Resources “Time to Cash”
Inconsistent Credit
Competitive Disadvantage
Underw riting Processes
No Single and Consistent
Source of Truth
Duplicate Data Entry
Inability to Respond to
Aging Systems Regulatory Requirements
Architecture Timely and Accurately
Poor Data Integrity
Lack of Integration w ith
Risk Appetite
Sub-Optimal Risk and Unintended Risk
Disparate Sources of
Performance Assessment Concentrations
Risk Data
Delayed Covenant
Tracking
Rear View Mirror Approach
Incomplete Portfolio
Management Reports
4
Credit Decisoning
5
The Future of Business Lending
Information Credit Credit Presentation
Gathering Assessment & Loan Structuring
Customer information is auto-populated Credit model and analysis requirements Where appropriate, credit presentation
from external data sources are automatically selected by borrower template is automatically selected
profile and needs
Pre-screening and peer benchmarking Financial information, third party
information is instantly available Most credit decisions are automated, with reports, and other details are
option for manual review automatically populated in the credit
Tasks and timelines are assigned
automatically Spreads, where needed, are automated presentation
Pricing is recommended based on factors
chosen by the lender
Data collection is automated, streamlined, and consistently applied
Workflow updates automatically based on completed tasks
Notifications are provided when tasks are due and past due
Time-in-process is tracked and analyzed to drive efficiency
Data is centralized and accessible for Customer credit quality and scores are Streamlined approval ,focus on outliers
business intelligence reporting tracked electronically based on data
feeds Required documentation is captured
Personalized dashboards enable real- electronically in the process
time tracking of risk, portfolio, process Customers receive automatic
metrics notifications when items are due A loan is packaged and sent electronically to
the customer for e-signature
Peer and industry data feeds to Credits for review are identified based
dashboards for benchmarking on score or behavior change triggers Signed documentation is stored and
archived
Monitoring & Portfolio Serv icing Decisioning &
Management & Collections Documentation
6
2 Meeting the Challenge
The CreditLens™ Vision
Customer Credit Credit
Management Analysis Presentation
Customer Engagement Credit Risk Solutions
Portfolio Risk Covenants/ Decisioning
Management Monitoring & Approvals
• A frictionless environment
• Consistent control of risk
• Seamless automation & integration
• Leverage new technology
8
Example: Illustrated Financial Spreading
Savings*
80% 7,500 225k-
Time Reduction Hours saved p.a.
375k
Equivalent cost saving
*Based on 50 w eeks, 25 RM’s each taking 2.5 hours to re-enter 5 years of financial statements in support of c3 review s per w eek. Costs based on $30-50 per hour.
Time per case drops from 2.5 hrs to 0.5 hrs.
9
Golden Source of Risk Data
Maintain a single, auditable golden source repository of credit and risk-
related data with workflow enablement to apply simple business rules,
such as automated rating model selection and mandatory data capture
10With Deeper Insight and Control of the
Entire Relationship
Entity Management
» Dedicated entity management
module provides core building
block
» Construct relationship
structures pivotal to accurate
risk assessment
» Tune and validate data capture
in accordance with entity type
– improving data strength and
quality
» Control and distribute risk
grades within a relationship
Provides a consistent and complete view for risk assessment
11Analytics
Powerful financial analysis and risk grading developed over
30 years
» Probability of Default and Loss Given
Default measures
» Industry standard and custom ratio analysis
» Multiple accounting templates available to
support regional and industry specific
accounting standards
» Integration with our +35 industry and
regional specific market leading RiskCalc
models, which leverage the largest global
database of private company financial
information
» Integration with internal, regulator approved
models, or statistical platforms such as ‘R’.
12Deal Structuring Screens
Facilities
Collateral
Guarantees
13Flexible Routing Patterns – to meet
most business needs
Resolve scenarios of different complexity
» Highly predictable and repeatable
business scenarios CreditLen’s
Scope
– Relatively small scale customer Hybrid
– Limited number of business departments
More Flexible and Automation
Business
– Repeatable business activities Process
Collaborative
» Unpredictable and unrepeatable
business scenarios
– Large scale customer Sequential
– A number of business departments
– Complex business activities without a
Business Complexity
SME Corporate
More Complex
standardized pattern
» Hybrid business scenarios
14Credit Presentation and Memo
Capturing the data and presenting in the Bank’s format
Credit Presentation & Credit Memo
Credit Presentation Module
Credit Memo (Output)
Credit
Presentation
Credit Memo Configuration
Credit Presentation Configuration
15Credit Presentation and Memo
Consolidate and inform
16CreditLens Covenants Meets the Markets
Needs
» Automate compliance checking
» Monitor early-warning indicators
» Improve credit origination practices
MARQ
Portal
CreditLens Integrated
Business Data
Modules Source
BvD
17Covenants Overview
18Tracking & Testing
An overall view of statuses of all active covenants
19Business Insights
Visualize data with integrated and intuitive dashboards for
different users from credit analysts to executive management
and auditors, providing business intelligence across your
team and the entire organization
20Deployment Options
Three approaches:
On Premise
» CreditLens is installed on
client site, as RiskAnalyst is
currently
Private Cloud
» CreditLens is hosted by
Moody’s infrastructure
Public Cloud
» CreditLens is hosted on a 3rd
party site
» E.g. Amazon AWS
» Datacenter in EU and or UK
21Model Authoring Platform
CreditLens Model Authoring Platform (MAP)
CreditLens
PD/LGD
Ratings
Model Inputs Model Outputs
R Server
23CreditLens Reduces Cost of Ownership
Powerful Open
Cloud Deployment Modular Licensing Efficient Upgrades
Configuration Architecture
SaaS, Private and Modular not Monolithic Configuration not Frictionless Standard Data Automation &
Commercial Cloud options Customization Product Upgrades Integration
CreditLens architecture puts the customer back in control
24Benefits to Risk Management
Regulatory Compliance Business Insight
Enforce high standards of Sharper, focused and
compliance in data governance, comprehensive data reports
credit assessment and risk driving better business
management decisions and allow ing
concentration on pro-active
risk assessment and
monitoring.
Process Efficiency Operational Risk
Increased collaboration, cleaner
Eliminate manual process and
data, improved communication and
financial statement re-entry
automated tools reduce the ‘time
and duplication reducing
to decision’ and increase
manual effort and increasing
productivity
the accuracy of data.
Consistency & Control Provisions & Losses
Consistent capture of financials, Increasing understanding of
approvals and overrides underpin credit risk and early detection
system integrity and helps of w arning signals through
simplify oversight and internal enhanced monitoring. Reduce
policy adherence. probability of losses via more
robust risk management
25Benefitting the Bottom Line
Revenue & Growth Technology/Cyber
Generate additional capacity to w rite Trust your critical business
more loans by reducing processing operations to advanced, reliable
time and increasing collaboration and proven technological
amongst the deal team architecture and IT .
Profit Culture
Better data governance, Transition to a more modern
accurate risk assessment and business environment utilizing the
increased transparency equals latest technology. Enables further
higher quality loans and positive cultural advancement in the
improved pricing for risk origanization
Credit Risk
Customer Satisfaction A modern w ay to originate,
Few er information requests, faster assess and manage credit
turnaround times and increased quality leading to better risk
time w ith Relationship Managers. Interact management and compliance.
digitally at a client convenient time
263 Looking Forward
Moody’s Analytics Spreading Solution
A comprehensive set of data, tools and services
BvD Orbis Tax Reader ML Spreading MAKS
Financials for Data extraction Automated data Automated Dedicated teams
public and private from customers’ extraction from tax spreading of PDF of spreading
companies general ledger forms documents analysts
28Bureau van Dijk
29Gather loan application data digitally and automate spreading.
MARQ™ online loan MARQ™ portal CreditLens™ / Lending Cloud
application Secure relationship management Automated spreading & scoring
Instantaneous account linking
Online & Desktop
BORROWER tools BORROWER-LENDER interface LENDER tools
30Spreading Automation
31Moody’s Analytics Spreading Solution
A comprehensive set of data, tools and services
BvD Orbis Tax Reader ML spreading MAKS
Financials for Data extraction Automated data Automated Dedicated teams
public and private from customers’ extraction from tax spreading of PDF of spreading
companies general ledger forms documents analysts
324 Alternative Data in
Credit ScoringMotivation
Role of Alternative Data in Credit Scoring and Decisioning
34Introduction
Understand credit relevance embedded in text
Text based Credit relevance Credit relevance
information model score
Company Research Early Warning Indicator Improve Models
Highlight relevant news Scan feeds to detect Improve traditional credit
and reviews increased risk risk models
35Method
Train model on historic text sources
Credit relevance
model
World leading Credit Risk
Cutting edge AI research
expertise, historic data and
models
Text Mining & Machine Large collection of machine
Learning techniques readable text
36Disclaimer
375 News
41
42
43
6 Social Media
45
46
Improve Default Prediction
Preliminary research yields promising results
52% 60%
Performance* Performance of
of RiskCalc RiskCalc
+
Sentiment
Scoring Model
* Accuracy ratio calculated on matched sample of firms with both Social Media
Reviews and RiskCalc EDFs containing 6588 observations with 41 defaults.
477 What’s next?
8 And Finally
Introducing the Data Alliance
Share Data, Gain Insight, Take Action
https://dataalliance.moodysanalytics.com/
A collaborative effort providing high quality credit risk insights
for portfolio-level benchmarking and data augmentation
Commercial & Industrial Project Finance
Commercial Real Estate Asset Finance
51Nelson Almeida Jamie Stark
Nelson.Almeida@moodys.com Jamie.Stark@moodys.com
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