Since the 2008 financial crisis, financial institutions face increasing regulatory compliance burdens and costs associated with analyzing the impact of new regulations on their underlying business and IT systems. They struggle with cumbersome, manual processes and over-reliance on expensive external consultants. A new approach is needed to help institutions more effectively assess regulatory impacts, identify required changes, and reduce costs through increased automation and reuse of impact analyses.

1. Semantic solution.
Regulatory impact analysis
January , 2015
Copyright © Acuity-space, 2015. An non-profit community for shared learning

2. 2
• Since financial crisis of US in 2008, newly introduced regulations and controls have mandated
changes that has impacts on underneath IT systems. Systemically Important Financial
Institutions (SIFIs) as well as mid-size banks, broker dealers and investment management
companies are over-burdened with compliance related IT expenses
• These financial service companies are struggling to assess the impacts & identify
remediations as there is no alternative to the cumbersome manual exercise
• Dependence on external subject matter experts (SME), most often external consultants
• Significant man & material cost
• Limitations to reuse, contributes to sky rocketing remediation cost
• Non-compliance isn’t an option
• Financial institutions are running against time to meet regulatory deadlines, incurring very
high CONSULTING and IMPLEMENTATION COST to analyze regulatory impact on their
BUSINESS & IT SYSTEMS
• In addition, the global regulations such as BCBS RDA, Basel II & III, EMIR( European market
infrastructure reform) have implications for non-US banks as well making this problem a
global phenomenon.
Financial reforms are altering dynamics of the banking
industry
Copyright © Acuity-space, 2015. An non-profit community for shared learning

3. Banks are faced with following
challenges:
•Burdened with cob web of business lines and IT
systems accumulated through various acquisitions
and mergers over last decade which has led to
formation of very large FRAGMENTED DATA of IT
portfolio
•Unable to discover correlation among business
activities ( business process, functions, events, rules
etc.) and corresponding technology components
•Lack of explicit understanding of regulations and
their systemic impacts on the businesses is
magnifying the SIZE of PROBLEM
•Conventional problem solving approach is
inadequate, un-scalable and extremely cost
prohibitive
An underserved area in the field of Regulatory Risk Transformation
Banks are burdened with FRAGMENTED DATA across
Enterprise
Copyright © Acuity-space, 2015. An non-profit community for shared learning
3

4. Metadata management
Knowledge capture is an integral part of impact assessment and
adoption of a metadata centric approach is becoming a trend..
Business Concepts
Physical data
source 1
Physical data
source 2
Physical data
source 3
Logicalmapping
A
B
C
D
F
• Unless business defines concepts, the logical
mapping is not possible
• Business is unlikely to be conversant with Ontology
structuring
• Why does business spend time to define the
business concept
A
A
B
• Presence of very large number of physical data sources
makes mapping a lengthy exercise
• Conventional mapping requires personal intervention,
making it effort intensive
B
refer
refer
refer
• Lineage between the business concept and the physical data source is an ever growing challenge for
banks
• In order to address the above mentioned problem, Banks are creating a metadata governance
structure for gathering risk informations across the lines of business and various asset classes
Creation of ontology in an automated fashion is every customer’s need
Copyright © Acuity-space, 2015. An non-profit community for shared learning
4

5. Connecting personal intelligence of the SMEs and create a contextual
Knowledge graph
A
B
C
D
X
F
How do we see the knowledge structure …
Front office risk
function
Middle office (Risk
function)
Risk IT
Finance
Finance IT
Copyright © Acuity-space, 2015. An non-profit community for shared learning 5

6. Semantic modeling and machine learning together discover actionable insight
from structured and unstructured knowledge pools. It can be done in near real time, on a
dynamic model that feeds existing information, knowledge and structure wisdom to
visually see the root cause and take right action to avoid business hour losses.
Semantic modeling : Knowledge capture
Ontology
Documented knowledge
Metadata knowledge
DB
DB
DB
App
Copyright © Acuity-space, 2015. An non-profit community for shared learning
6

7. Information structuring : Ontology design perspectives
• A Bank is in essence an IT Company
• Software manufacturing
• Data production, consumption
• Information supply chain
• So how do we manage the business view of data?
• Language interface business to IT – Conceptual model
• Putting some thing into RDF/OWL does not make it meaningful
• Only you can do that
• So,what is a meaningful model
• Formal relationship between model and subject matter:
• “Everything is a Thing”
• Formal notation grounded in common logic
• Abstraction of kinds of thing into their simplest possible
building blocks
• Contracts, Parties, Legal Entities etc.
Copyright © Acuity-space, 2015. An non-profit community for shared learning
7

8. Assistive IntelligenceDomain Knowledge
Key levers
Financial risk
(all Asset Classes)
Regulations
(US and Europe)
Ontology
Engineering
Natural Language
Processing
• Regulatory impact assessment & analysis on the banking portfolio
• Risk data aggregation - as defined Basel committee (BCBS), Dodd Frank SCCL(Single
counterparts credit limit)
• Self correction of domain policy
• Create your Ontology
Solves
Copyright © Acuity-space, 2015. An non-profit community for shared learning
8

9. Contact
Rajib
K
Saha
saharajib2001@yahoo.com
+1-­‐609-­‐225-­‐1049
Copyright © Acuity-space, 2015. An non-profit community for shared learning
9