accuity.com

Fircosoft Best Practice Webinar:Tuning Your Filter

Zayd Sukhun, CAMS CFCS

Agenda

• What is Tuning?

• Preparing for Tuning

• Tuning Strategy

• Exceptions

• Business Rules

• General Thoughts

What is Tuning?

What is Tuning?

“ Filtering data against a given list with default filter settings, and

conducting iterative changes to reduce the proportion of false

positives to a reasonable level”

What is Tuning?

A risk-conscious and systematic approach to adjust filter results Reduce false positive alerts

Identify low quality filter input

Facilitate operational requirements

Accommodate monitoring requirements

When to Tune

Filter

upgrade

New Line

of

Business

List Update

New to

Fircosoft

New

Resources

file

Preparing for Tuning

Who’s involved?

• Provide product expertise and experience-based

suggestions

• Analyse hits and determine appropriate

methodology

• Understand Customer environment and reflect

risk profile in suggestions

• Perform tuning to improve quality of hit results

Who’s involved?

• Compliance expertise and regulatory context

• Instruct on company risk guidelines

• Express desired false positive reduction techniques

• Provide current settings, list, rules

• Knowledge of data content and purpose of fields and transactions

Compliance

Who’s involved?

• Provide alert based workflow requirements

• Share details of recurring false positive hits

observed by usersBusiness or Operations

Who’s involved?

• Provide production data for tuning

• Set up tuning environment for consultant

• Prepare tuning data into appropriate formatTechnology

Resource Availability

First Day

kick-off and setup (2hrs)

Each Day

end of day & adhocreview

Follow-up

presentation of tuning (2hrs)

Compliance Compliance Compliance

• Validate format

• Manageable-sized files

• Sufficient historical data

• UTF-8 without BOM

• Description of data fields

Data

Tuning Prerequisites

• 3 PCs

• 80GB hard Drive, 4-16GB RAM, 2GHz processor

• Shared network from all PCs

• No overnight restarts

• Firco Classic

• FML & FMM

• Good text editor (e.g. Notepad++)

• Firco Utilities user for consultant

• Microsoft Office (Excel, Word)

Environment

• Up-to-date list (KZ

format)

• Up-to-date Resources file

• Existing FML rules

Fircosoft Filter

Tuning Pre-requisites

• Filter and Utilities Application Training

• Filter theory & algorithms

• FFSU1: Understanding Filter Fundamentals

• FFSU2: Hands-On with Firco Filter Classic

• FMLU1: How to Create Business Rules in FircoMultiLaws Manager

• FMMU1: How to Manage Lists of Blocked-Entities in Firco MultiList Manager

Certified Training

Responsibilities

Initial Tuning

• Thoroughly review and understand Fircosoft suggested tuning approach

• Evaluate risk of all rules and test to identify kept and lost hits

• Assess impact of algorithm and filter settings on the data

• Test effectiveness of exceptions

Speaking of Testing!

Deliverables

Presentation

• Review of conducted tuning as required by client

Report• Outlines context for tuning

• Baseline hit statistics

• Iterative results after applying algorithms and rules

• Summary of risks and impact of tuning concepts with examples of retained and lost hits

Rule & Exception Files

• Suggested and sample rules and exceptions are provided

Responsibilities

Ongoing Tuning

• Maintain good quality list

• Periodically review exceptions & rules

• Replenish rules as necessary

• Update the Resource File upon release

• Test new lists, rules and resources files against production data

Tuning Strategy

Tuning: Conceptual Overview

Filter Engine

Preparation

FML.rul

FOFDB.kz

Message

s or

Custome

r Data

Decision Interface

FMM

FML

Reduce

%FPH ?

What are false positive hits?

• False alerts on the global volume of transactions after validation by operators

• Influenced by:

List volume

Languages in data

List quality

Transaction volume

Filter settings

Type of ListLocation of company

Clients and market served

Tuning Concepts

Common Words

Generic common words and places which hit but the context in which they occur is inappropriate

Acronyms

Frequently occurring abbreviations and acronyms of length 4 or less

Common Names (Individuals)

Hits against single word names for individuals with little other information to support validity of hit

SAID

SUCCESS

BANK

GENERAL

BUT

FACT

HUA

AIR

SEP

ETA

NOV

LIT

JOSE

JAMIE

MIKE

MARIA

CHARLES

OMAR

Tuning Concepts

Companies

Address common terms or phrases generally matching against company names

Codes

Reduce hits against passport, national ID and other codes which are inappropriate due to expected content of data

Vessels

Handle misspellings against vessels and and reduce scope of vessel hits as applicable to data.

CAPITAL

REAL EST

SPECIAL

GLOBAL

BUSINESS

REF INV

1987

UNKNOWN

VICTOR

CRISTINA

SUNSHINE

BLUE

ATLANTIC

DIAMOND

False Positive Hit Reduction Strategy

Broad Strategy

• Specific filtering features: tags, types,

format, units...

• Exclude Origins, Designations,

Keywords, Types…

• Exclude tags (transactions only)

• Disable/enable algorithms

• Exclude low-value synonyms

(“weak aliases”)

• Generic exceptions and rules

Narrow Strategy

• Specific filtering features: tags, types,

format, units...

• Customer-specific rules

• Customer-specific exceptions

Tuning with Classic

• Review hit rate

Tuning with Classic

• Review heavy hitters by name – manage with rules and exceptions

Exceptions

Functional Exceptions

• Since V5.7 – fuzzy or exact matching can be set

• Name must be longer than listed entity

Vessel “BROTHERS”

matches 5 times/day

Against reliable customer

MICHEL BROTHERS

Customer Exception

BROTHERS, MICHEL

MICHEL BROTHERS

BROTHERS

Technical Exceptions

• Since V5.7 – fuzzy or exact matching can be set

• Name must be longer than listed entity

Synonym “SAID”

matches 10 times/day

against Strings in transaction

like “He said” or “She said”

or “Customer said”

Technical Exception

HE SAID / SHE SAID /

CUSTOMER SAID

HE SAID / SHE SAID /

CUSTOMER SAID

KHALIL, JARRAYA

(aka SAID)

Exception Good Practices

Important to restrict the impact of the exception

• Use Hide ID field

• Allows a targeted control of the exception impact

• If several record IDs, use blank as separator

Business Rules

FML Action Types

Hooks Actions Description

Before filtering No checking • Irrelevant messages are disqualified from filtering

During filteringBlocking/

Non blocking

• Rules for monitoring. Alerts (Blocking or non blocking) are forced

on the message content (not on the list)

After filtering

Ignore hit

Non blocking

Qualify hit

• Alerts detected by the filter are not relevant. They are ignored.

• Alerted messages are not blocked. A trace is kept in a report

(monitoring)

• Hits are branded with priority/confidentiality levels to facilitate

decisions in the validation interface

Rules Agenda Trigger

• Rule calling another rule or set of rules

• Allows instituting a hierarchy in conditions

• Avoids repeating the same condition on several rules

Rules Agenda Trigger

ACTION

ETA

SEP

HUA

NOV

TRIGGER 1 CONDITION: ‘APPCODE IS SAA’

APPLY AGENDA: BY_TYPE

CONDITION: ‘TYPE IS COMPANY’

APPLY AGENDA: ACRONYMS

RULES

ACTION

TRIGGER 2

Business Rules – Organization Sample

Business Rules – Good Practices

Restrict the impact of the rule

• Record ID, Origin, Designation (whenever possible)

Naming convention for rules or agenda

• Numeric ordering, Short, clear, no special characters

Using a negative operator can sometimes make a rule simpler (IS NOT,…)

Use Trigger Sets whenever possible

General Thoughts

General Good Practices

Review Hit Reduction Strategies Annually

Find genuine equilibrium between risk appetite and hit reduction strategy

Maintain separate rule files for CIF and Transaction filtering

List of good quality

• Test final list before putting it into production

Questions?