MarkLogic & ISDA: Rethinking The Trade Lifecycle

© COPYRIGHT 2014 MARKLOGIC CORPORATION. ALL RIGHTS RESERVED.

Rethinking the Trade Lifecycle

Diane Burley MarkLogic

Ken Krupa MarkLogic

Andrew Parry ISDA

© COPYRIGHT 2014 MARKLOGIC CORPORATION. ALL RIGHTS RESERVED. SLIDE: 2

ISDA/FpML XML Standard protocol (1992) Describes financial instruments Describes Business Processes Tree-structure Highly flexible Normalizes instruments to be both

human and machine readable

MarkLogic Enterprise NosQL Established 2001 Multimodal (XML, JSON, RDF) Maintains Tree-Structure Transactional Secure Scalable Search built into the stack Gartner: #1 Operational Data

Warehouse

Agents


Trade Lifecycle (Simplified)

Front Office

Trade capture Execution

Middle Office

Validation Booking

Confirmations

Back Office

Clearing Settlement Accounting

Pre-execution

Record Retention

Surveillance, Risk, Compliance

Enterprise Reference Data


Trade Lifecycle (Simplified)

Front Office

Trade capture Execution

Middle Office

Validation Booking

Confirmations

Back Office

Clearing Settlement Accounting

Pre-execution

Record Retention

Surveillance, Risk, Compliance

Enterprise Reference Data

YOU ARE HERE


• Life-cycle events and processes • Settlement • Payment events • Reversal, exercise, maturity • Reporting

After Execution

Clean-Up

• Confirmation and matching • Data enrichment

o May be economic or non-economic

o Settlement info, custodian info, etc.

• Cash-flow definition • Recording of other fees • Cancels, corrections,

amendments (also life-cycle)

Process


Example: Equity Derivative

Trade Date

Option Style

Option Type

Seller Buyer Underlying #Options … Strike Price

Premium …

15 Oct 2014 American Call Party A Party B IBM 150,000 … 190.00 500,000 USD

…

• Relatively flat – but wide • Maps well to tabular structure • WARNING: too much variability causes sparse data headache


Example: Interest Rate Swap Interest Rate Swap

Type: Vanilla Leg-1

Cashflow

Type: Fixed

Notional

Cashflow

Cashflows

Payment Date

Payment Date

Payment Amount

Payment Amount

Counter-party

Cashflow

Type: Floating

Cashflow

Cashflows

Payment Date

Payment Date

Payment Amount

Payment Amount

Counter-party

Leg-2

Hierarchical Requires shredding for

RDBMS Potentially long life-

time


Internal Cleanup and Related Reporting Clean up of ‘good’ trades

Ensuring the correct counter-parties (reference data lookup) Adding data that wasn’t captured at execution

Clean up of ‘I didn’t mean that’ trades

Cancel a trade Correct or amend (incorrect values, incomplete terms)

Reporting/looking for anomalies

Assess risk by time-based analysis of trade changes, both within lifecycle of individual trades and across trade lifecycles of related trades Consider bitemporal aspects of trade event and reference data:

Allow replay of trade queries as they would have been at a point in time Allow the identification of the version of reference data that was relevant at the point in time


Conventional Solution: Waterfall

Identify • Identify

sources • Plan

Model • Logical Design • Physical Model

Integrate • Stage • ETL • Data Quality

Discover

Time


Waterfall vs. Agile Modeling

Identify • Identify

sources • Plan

Model • Logical Design • Physical Model

Integrate • Stage • ETL • Data Quality

Discover

Time

🔍

Textual

Structured

Multi-media

Geospatial “ ” Social

Semantic

Discovery/ Model Loop


API

R1

R2

R[n]

Cancel Correct Amend

Dodd-Frank UnCleared

Margin

Other

Upstream Trading Systems Enterprise NoSQL Tradestore

Downstream Reporting Systems

Regulators

Ingestion Trade Validation

Trade Standardization


Functional Benefits Enterprise NoSQL can handle many data shapes at once

No need to force-fit into a canonical model Reduced or no ETL New data immediately searchable

More natural representation of business entities

Shredding of data into 3NF not required Self-describing schema

A more agile platform

Trade decoration is easily handled As regulations change, new information may be incorporated more easily

As-is and as-was handled more easily

Bi-temporal data/values in more natural form (again, no shredding) Bi-temporal schema handled (impossible with RDBMS)


Operational Benefits Cross-cutting indexes regardless of data shape

New terms are searchable, typically without DBA intervention

Scale-out model to handle volume Distributed shared-nothing architecture Linear scalability Mixed workloads are OK

Maintainable, elastic and cloud-ready

Most administrative events are hot operations Not just scale-up but scale-down

Lower maintenance overall

Search & Query ACID Transactions High Availability / Disaster Recovery Replication Government-grade Security Scalability & Elasticity On-premise or Cloud Deployment Hadoop for Storage & Compute Semantics Faster Time-to-Results

The Only Enterprise NoSQL Database

APPLICATION SERVICES

DATABASE SEARCH


Enterprise NoSQL Operational Tradestore Faster Time to Market New instrument types can be supported in a

matter of hours rather than days, weeks or even months.

Higher Data Quality A unified Operational Trade Data Store eliminated processing errors associated with multiple stores containing conflicting data

Lower Cost per Trade Hard and soft costs associated with maintaining 20 different systems were eliminated (FTE support staff reduced from 10 to 1), and expensive exception management costs were avoided.

Greater Agility Changing business needs such as escalating regulatory pressures can be met without the need for adding expensive resources

Enterprise NoSQL Education

Read

nosqlfordummies.com

Listen Learn Meet

marklogic.com/training [email protected] www.po.st/D11POH

© COPYRIGHT 2014 MARKLOGIC CORPORATION. ALL RIGHTS RESERVED.

Thank you! For more information:

[email protected]

Date post:	27-May-2015
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MarkLogic & ISDA: Rethinking The Trade Lifecycle

Technology