20170817 fico - the evolution of regulatory compliance ... · fico aml advanced analytics feature...

© 2017 Fair Isaac Corporation. Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.

18th Annual Conference Compliance Institute SA –Shaping the Future of Compliance in Africa17th August 2017

The Evolution of Regulatory Compliance into the use of Analytics

Jürgen KriegDirector Solution Sales - FICO TONBELLER

© 2017 Fair Isaac Corporation. Confidential. 2

Jürgen Krieg

• Savings Bank Ulm, Germany

• MBA, Henley Business School

• 12 years selling Core Banking Systems

• 4 years selling regulatory Compliance at FICO TONBELLER, Heading the EMEA sales team

• Living in Mannheim, Germany


Closed Loop: The GRC Framework for Regulatory Compliance

GOVERNANCE

Tone of the Top

Company Strategy(geographic markets & customer groups,

products & services, sales channels)

Risk Assessmentdescribes and assesses risks

- abstract- categorized

- model based

Compliance ResearchImplementation of the risk in scenarios

- precise- individual / person - related

- data - based

Sing

le C

ases

-Es

cala

tion

Upd

ate

Ris

k As

sess

men

t

RISK

COMPLIANCE

Code of conduct

Ris

k &

Com

plia

nce

Coc

kpit

Risk Appetite

Implementation


AML Analytics Maturity Model

Level 1Rules and basic analytics

• Rules-only KYC customer decisioning and transactional monitoring• Basic analytics capabilities• Investigate alerts

Level 2Enhanced AnalyticPrioritization

• Prioritize alerts based on analytic model scores• Challenge KYC-based segmentation with transaction-based

behavior analytics• Still rules-first approach

Level 3Analytics Scores-first

• Scores first, rules second. • Analytic model drives case-working• Reduced set of rules• Real-time blocking of highest risk transactions

Level 4Adaptive models

• Analytic models with adaptive feedback • Recent SARs influence scores• Advanced investigation based on customer transaction similarity• Rules to respond to regulatory typologies


Key Challenges of Customers

1. Cost pressure (TCO)2. Efficiency of processes3. Volatility of regulatory requirements4. Increasing data volume5. Unknown risks and scenarios6. Uncertainty of legal risk7. Legal pluralism (regulatory compliance, data protection, different jurisdictions etc)8. Heterogenous IT-environments and data9. Convergence of different compliance disciplines10.Big Data (structured, unstructured, SNA)11.New technologies (Analytics, AI/ML, SNA, text analysis)12.Simulation of decisions, optimization of scenarios13.Shorter reaction times / real-time14.Data quality15.Risk Assessment (completeness, context, adaptation)16.Heterogeneous reporting requirements


How Analytics Can Support in the Various Steps of the Compliance Process

Risk Assessment Customer Acceptance

Generation of Alerts Investigation

1. Risk prognosis for new customers based on models build from historic data

2. Identification of relevant attributes with strong relevance for prognosis

1. Identification, quantification and modelling of risks

2. Continuous monitoringof risk landscape (in real-time)

3. Development of best practices (consortiummodel)

1. Identification of behavioural changes and outliers (soft clustering misalignment)

2. Learning from true positives (filed SAR) and false positives (Aml threat score)

3. Alert on suspicious relations/networks from SNA

1. Priorisation of alerts using analytics (SCM)

2. Identification of similar cases and proposal of workflows

3. Provision of additional data to support investigation

4. Automatic execution of notebooks (prepare data and workflows)

5. Social Network Analysis to support investigation

6. Automated closing of low priority alerts

7. Special investigations based on FIU requests (SNA)


Overview of the systemSiron®Anti-Financial Crime Solutions


Siron®Anti-Financial Crime solutions providing an End-to-End KYC approach

Business and Organization

Know your CustomerCustomer Acceptance

Ongoing Due Diligence

Security and Technology Usage

Statistics and lessons

learned

Enhanced CDD Simplified CDD

3rd PartyDatabase Provider

PEP

WL / sanctions…

Beneficial Owners

ICIJ Offshore Leaks Date Base (incl. Panama Papers) Risk and

Compliance CockpitCase Management and Regulatory Filing

Compliance Requirements40/9 FATF recommendations, 4th EU ML directive, BSA, Sarbanes Oxley Act, …

Training and Awareness program

,independent audit

Man

agem

ent C

omm

itmen

t Processes Initiatives Policies …

Anti-FraudCounter-Terrorism FinancingAML

Business Risk Assessment

FATCAand AEOI (CRS)

Reporting


Sanctionswatch lists

(EU, OFAC, UN)Bad

pressBeneficial

ownerOffshore Leaks Database (incl. Panama Papers)

PEP screeningvia 3rd party

data

Counter Terrorism Financing

?

3rd Party Applications and Data

Risk Rating(can be defined

individually)

Flexible,Dynamic KYCQuestionnaires

Not acceptable

High

Medium

Low

CustomerAcceptance

ContinuousInvestigation

(ML)Every 5th Year*

Every 2nd Year*

Every Year*

CustomerAccepted

* Numbers can differ by country/region

ContinuousScreening

(Fraud)

Use Case of KYC lifecycle approach – integrated 3600 view of client risk

KYC

EMB

AML

FALCONReview:


CustomerAccepted


(EU, OFAC, UN)Bad

pressOffshore Leaks Database (incl. Panama Papers)


data


Beneficialowner ID Verification


?


individually)


(Siron®KYC)

Not acceptable

High

Medium

Low

CustomerAcceptance Continuous

InvestigationEvery 5th Year*

Every 2nd Year*

Every Year*


ContinuousScreening

Siron®KYC – 1st Step: Initial Risk Rating

KYC

EMB

AML

FALCONReview:

No matchesNo matches

Initial profile = low risk

Initial profile = low risk

New customer to answer

questionnaire

New customer to answer

questionnaireLow risk

indicatorsonly

Low risk indicators

only

”Low risk“ set of scenarios will

be used

”Low risk“ set of scenarios will

be used


CustomerAccepted


(EU, OFAC, UN)Bad



data




?


individually)


(Siron®KYC)

Not acceptable

High

Medium

Low



Every 2nd Year*

Every Year*


ContinuousScreening

Siron®KYC – 2nd Step: Ongoing Behavior

KYC

EMB

AML

FALCONReview:

New risk category =medium risk

New risk category =medium risk

Unexpected / unusualtransaction

Unexpected / unusualtransaction

“Medium risk“ set of scenarios will be used“Medium risk“ set of

scenarios will be used


CustomerAccepted


(EU, OFAC, UN)Bad



data




?


individually)


(Siron®KYC)

Not acceptable

High

Medium

Low



Every 2nd Year*

Every Year*


ContinuousScreening

Siron®KYC – 3rd Step: New Information From 3rd Party

KYC

EMB

AML

FALCONReview:

New BO to beconsidered a PEP

New BO to beconsidered a PEP

Change ofBeneficial Owner

Change ofBeneficial Owner

New risk ratingaccording to PEPstatus = high risk

New risk ratingaccording to PEPstatus = high risk

Enhanced due diligence

Closer monitoring;“high risk“ set of


Enhanced due diligence

Closer monitoring;“high risk“ set of



CustomerAccepted


(EU, OFAC, UN)Bad



data




?


individually)


(Siron®KYC)

Not acceptable

High

Medium

Low



Every 2nd Year*

Every Year*


ContinuousScreening

Siron®KYC – 3rd Step: Behavior Monitoring

KYC

EMB

AML

FALCONReview:

Re-classificationRe-classification

BlockingTransaction

BlockingTransaction

Enhanceddue diligence

Enhanceddue diligence

FraudulenttransactionFraudulenttransaction


CustomerAccepted


(EU, OFAC, UN)Bad



data




?


individually)


(Siron®KYC)

Not acceptable

High

Medium

Low



Every 2nd Year*

Every Year*


ContinuousScreening

Siron®KYC – 3rd Step: Behavior Monitoring

KYC

EMB

AML

FALCONReview:

OFAC match leadsto new category= not acceptable

OFAC match leadsto new category= not acceptable

OFAC matchOFAC match

New risk category =not acceptable

New risk category =not acceptable

Blacklistingof customerBlacklistingof customer

Terminatecustomer and

close monitoring

Terminatecustomer and

close monitoring

Blockingtransactions

Blockingtransactions


“What is unusual behavior?”

“In future we will see the combination of rule-based detection methods with Advanced Analytics”

Basic profiling capabilities (already part of any Siron®AML installation):

1. … compared to predefined thresholds

2. … compared to stated behavior during KYC onboarding

3. … compared to historic behavior (e.g. dormant account with sudden increase…)

4. … compared to peer group

Advanced Analytics:

5. … based on FICO patented machine learning capabilities…


Siron®AML Workflow

Operational Systems• Daily

Data• External

Data

Siron® AML

ETL Process

Audit• Scenarios• Analysis• Investigati

ons• Reports• Actions• ...

Transactions

Accounts

Customers

Alerts, Investigation & Case Management• Monitoring• Analysis• Reporting (FinCEN)

Configuration• Set of Scenarios• Customer Groups• Analytics Setup

Profiles/ Statistics

Scoring processAML

Advanced Analytics

Model

Detection Scenarios

Analytics Scoring


Siron®Anti-Financial Crime Solutions provide analytics capabilities

• Objective: Generating alerts at Low False Positive Rate• Allowing easy setup and configuration of detection scenarios by the end-user• Allowing customer ongoing risk classification• Based on customer data, account data, transaction data, non-monetary events and profile data• Peer-Group Profile comparison. • Historic comparisons

• Profile Base Data:• Sum, Number, Min, Max, Average and Standard Deviation• Per transaction type• Per account / customer• On a monthly bases• E.g. Sum/Number/Min/Max/Average/Std Deviation of foreign transactions per account per

month


FICO Siron®AML Advanced Analytics:

Current product features and integration


FICO AML Advanced Analytics

Feature Description Benefit

AML Soft Clustering Misalignment Score

Low Scores – Aligned with peer group, normal behavior. Lower risk.

High Scores – Different from peer group, abnormal behavior. Higher risk or need to revisit KYC.

Reason Codes – Explain why model produced score

Find bad actors based on misalignment with clusters. Complements existing rules-based KYC solution.

No historical SAR data required to train score.

AML Threat Score Low Scores –Behavior is normal for peer group. Not similar to SARs.

High Scores – Suspicious money-laundering activity, similar to historical SARs. Unusual behavior.

Reason Codes – Explain why model produced score

Find more suspicious behavior. Reduce risk of regulatory fines

Prioritize Alerts - Identify which alerts are more critical and investigate first

Improved efficiency of compliance efforts.

Two scores can be used together or independently. Scores from 1-999


How Analytics delivers value to clients

• AML Soft-Clustering Misalignment (SCM) Score is high for customer whoareoutliers from their peer group and risk segmentation.

• High AML SCM Scores are correlated with known SARs, and also can detect unusual behavior which hasn’t been incorporated in rule scenarios. Better compliance by detecting suspicious behavior before it is a well-known scenario. Lower risk of fines.

• Find legitimate customers who are acting differently than peers. Customers may be underserved by current banking products. Marketing opportunity and improved customer-lifetime-value by updating KYC.

Improve KYC process


How Analytics delivers value to clients

• Alert Prioritization: Detect suspicious activity faster─ Compliance team investigates based on combination of AML Threat Score and rule

firing. ─ AML Threat Score often increases before a rule/scenario is triggered.─ Reduced regulatory risk by detecting illicit activity sooner

• AML SCM score can also be used for Alert Prioritization─ When lack of SAR training data prevents AML Threat Score from being built.

Find more suspicious activity, and stop it faster


Consulting to operationalize Analytics

Scores and Rules

Model installation

Data and profile validation

Model go-live

Consortium data sent to FICO

Retrain model

AML Analytics Process

Statement of Work/ Agreement on

Scope

Kickoff meeting anddata dapping

New client


AML Analytics Maturity Model

Level 1Rules-only

• Rules-only KYC customer decisioning and transactional monitoring• Work all alerts

Level 2Analytic Prioritization

• Prioritize alerts based on analytic model scores• Challenge KYC-based segmentation with transaction-based

behavior analytics• Still rules-first approach

Level 3Scores-first

• Scores first, rules second. • Analytic model drives case-working• Reduced set of rules• Real-time blocking of highest risk transactions

Level 4Adaptive models

• Analytic models with adaptive feedback • Recent SARs influence scores• Advanced investigation based on customer transaction similarity• Rules to respond to regulatory typologies


Summary


Differentiators of Siron®Anti-Financial Crime Solutions

• Standard end-to-end software with highly flexible configuration capabilities

• Solution is combining:• Rule based checking• Pattern recognition• Profiling based detection• Link analysis• Advanced Analytical methods (as of version 18)

• One system for a whole corporation –Applicable for banks of all size and type of business

• Holistic 3600 customer risk view, alert and case management

• Investment protection (Hard- and Software, Knowledge) by use of existing system and database technology

• Best-in-class Multi-tenant capability

• Available on-premises / in-the-cloud / hybrid

• Largely automated surveillance

• Sophisticated case management workflow

capabilities (as of version 17)

• System-independently integrated into the IT-

infrastructure

• Scalable performance even for highest volume

of data

• Highly flexible support of any individual

definition of suspicion criteria

• End user-friendly dialog

• Audit trail protocols of all operations and

definitions

• Best-in-industry price-performance ratio

• Easy to update to future versions at low costs


Highlights using Siron®Anti-Financial Crime Solutions

Secure from Reputational Damages

Live in Peace with the regulator (incl. 314a Search and 504t)

Understand client structure and Risk

Ready for next generation AML

Secure from Fines

Low TCO via a standard solution with rich configuration capabilities


Jürgen Krieg

• Savings Bank Ulm, Germany

• MBA, Henley Business School

• 12 years selling Core Banking Systems

• 4 years selling regulatory Compliance at FICO TONBELLER, Heading the EMEA sales team

• Living in Mannheim, Germany

© 2017 Fair Isaac Corporation. Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.

Thank YouJürgen KriegDirector Solution Sales – FICO TONBELLER

[email protected]

20170817 fico - the evolution of regulatory compliance ... · fico aml advanced analytics feature...

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