Cognitive Finance

L Venkata SubramaniamSenior Manager, Knowledge Engineering and Data PlatformsIBM Research - India

Cognitive Finance

L Venkata SubramaniamSenior Manager, Knowledge Engineering and Data PlatformsIBM Research - India

Financial Technology

Timeline

1950sCredit Cards

1960sATMs

1970sElectronic Stock Trading

1980s Mainframe

1990sInternet & eCommerce

2000sOnline Banking

2010sFinTech Disruption(Cloud, Social, Mobile, Analytics)

New services are emerging and new markets are forming for banks

Increasing regulationEmergence of new competitors (digital

players)Inexperienced advisor

workforce

59% of industry executives agree that increased regulation will force them to fundamentally change their business model

1/3 of firms globally spend at least a whole working day per week tracking regulatory change

1/3 of traditional bank revenues could erode from competition from non-banks by 2020

55% of bank executives view non-traditional players as a threat

25% churn in the advisor population

33% of the advisor population is new to their job (<2 yrs) in India

Global focus on fee-based business

6 of 10 of the world’s top investment banks have announced strategies to focus on WM & AM and deprioritize IB, S&T

14.5% AUM growth over the last 5 years has been for RIAs

fees

Optimize Offers and Cross Sell

How can I anticipate customer needs and deliver more timely,

relevant offers?

Financial Risk Management

How can I improve financial risk management to meet regulatory

demands and achieve better performance?

Fraud and Financial Crime Management

How can I better predict, detect and investigate fraud and

financial crime?

Leverage Payment Insight

How can I monetize payment information while

lowering costs?

Optimize Financial Performance

Leverage Social Media for Customer Insight

How can I gain new customer insight from social media data?

Proactive Customer Service

How can I anticipate customer issues and resolve them more

efficiently?

Operational Risk Management

How can I better identify, monitor, and analyze operational

risk across the enterprise?

Improve Incentive Compensation Mgt.

How can I optimize employee compensation to improve performance and increase

satisfaction?

How can I drive profitability and improve business

flexibility?

Create a Customer Focused Enterpris

e

Drive Agility

and Operation

al Efficiency

Optimize Risk and Complian

ce

Key imperatives and specific use cases where banks are focusing efforts

Imagine a World Where Data Is Simple

What if… …so you could…

all data from everywhere was available to all roles to drive insights and results

shop for the right data as easily you shop for next mobile phone.

you could quickly adopt and integrate the latest innovations into your systems to gain advantage

use new open source technologies that prove valuable as easily as plugging in an appliance.

you could use data anyway you want – from freedom of discovery to established reporting

have the right balance between business flexibility and governance and security.

you could rapidly launch new web and mobile apps and connect them to analytics so they could optimize behavior in real-time

build new businesses based on that unique advantage.

Imagine if you could do this … and drive your business based on deep insights

Technology is changing rapidlyBreadth of Insights to Enable Decisions

How can everyonebe more right…….more often?

Descriptive

Prescriptive

Predictive

Cognitive

What has happened?

What could happen?

How can we achieve the best outcome?

Tell me the best course of action?

Business ValueInsights

Banking in the Cognitive Era

Growth ProfitabilityEfficiency

CognitiveOperations

CognitiveAnalytics

CognitiveEngagement

Cognitive OperationsDrives a simpler, leaner organization that can make faster decisions and is closer to customers.

Cognitive Analytics Applies machine learning algorithms to mine big data for trends, real-time behaviours, predicted outcomes and optimal responses.

Cognitive Engagement

Aligns with a customer’s economic choices and optimizes the customer interaction and experience with the Bank.

Regulatory ComplianceCan XYZ small finance bank open up a new branch in location ‘L’?

– Regulatory Constraints on Small Finance Banks

– Is Location L an unbanked rural center or a prescribed district

– What %age of branches are in unbanked rural centers

8

iAssist: Automated Customer ServiceHello Watson, How are you?

Good! How May I help you?

I bought the home content insurance and I want to know if it covers for the

damages caused by flood

Yes, it does. The value of the goods protected depends

upon the premium

And, damages caused by lightening?

No, it only covers against storm, rainwater, flood and wind

Oh! How dearly is it going to cost me?

SpeechActs

Variations/

Anaphora

Ellipsis/Context

Cognitive Assistant

Issues successfully handled by Cognitive Automation

Customer logs a ticket

Customer calls IBM support

Tickets

Fall back to agent (Chat)Unsuccessful

Agen

ts a

ssist

ed b

y Co

gniti

ve A

ssist

ant

At the Back In the Middle In the Front

Ticket Management

System

• Monitors and Learns resolutions from Agent Chat

• Identify actions/steps which can be automated

• Disambiguates User context• Answers queries with resolution• Executes automated resolution

• Automation Discovery and Execution

• Automation Scheduling • Proactive Anomaly

Detection• Answers queries with

resolution

Prevent Tickets / Problems / Alerts by Monitoring on Alerts / Warnings / Events

10

Application & Infrastructure

Watson for IT Landscape for Cognitive Automation Strategy

Watson provides the opportunity to radically change the application support experience

11

News & AlertsNews and alerts pertaining to the user that popped up in the system since the

last time the user logged on.

New KB ArticlesKnowledge Base Articles relevant to the area that this user works in, and related to the things that this user

asked about.

Personal ProfileWork location, preferences, and other

relevant information that provides context to the questions this user is asking.

System UpdatesUpcoming system updates that have a possibility to affect this

user’s workflow.

Productivity TipsBrief tidbits of information that can assist the

user in avoiding certain common problems observed by others around this user.

Recommended online training based on patterns of questions asked

Relevant TicketsTickets relevant to the questions this user had in the past and that

are related to events happening in the system.

PeoplePeople near this user, including super-users,

who can assist with problems that this user is encountering.

Virtual AgentChat interface to provide answers to user

queries and problems

The New World of Risk – Linking Credit Risk and Customers’ Reputations

Stru

ctur

ed

Info

rmati

on

Inte

rnal

Un

Stru

ctur

ed

Info

rmati

on

Exte

rnal

U

nStr

uctu

red

Info

rmati

on Assessment

Extraction

Events

Actions

Intentions

• Model Building

• Heat Maps• Portfolio

Mgmt

Inte

grati

on

• Linkages• Trends• Sources• Locations

SentimentsCustomers’ Reputations

Credit Risk

KRIs

Financial Losses

Cred

it m

anag

emen

tBa

ckte

sting

Segments, Financials, Transactions,

•Text Analytics

Map

ping

& R

outin

g

Analytics

While eventually the early trends in unstructured data will surface in structured metrics. The trick is to capture early.

13

We will use iterative approaches whilst dealing with data

Identify Topics and Issues

to monitor

User definesanalytical models

(Rule Editor)Sources

Issues

“Only selected segments “snippets” of an article which discussed the intersection of the topic are selectedCo-occurrence

Analytics

Trend by monthTopic vs Entity

Topic Classification

SentimentClustering

User interacts with data to discover

insight

new topics

Dashboard Analysis Reporting

Extract Blogs, Boards, News

sources, Forums, Complaints, NGO’s, CRM,

and Internal structured data

Strong, Weak, Emerging Signal Alerts

Trending and SentimentAnalysis

Companies

Sectorial View

Iron and Steel

Market

Government

Global

Oil & Gas

Market

Government

Global

E-Commerce

Market

Government

Global

Sector Wise Company Performances

Iron and Steel

ABC steel

XYZ Steel

MNO

Oil & Gas

OPQ

RST

UVW Oil

Infrastructure

ABC

FGH

GHI

Natural Language Querying

How have institutional investments changed in Alibaba since 2010?

 100 million+ facts ~140,000 Company objects 350,000 Person Objects >30 relationship types

Financial Ontology

Financial Knowledge Graph

Natural Language Query

Semantically parse the NLQ into an Ontology Query (OQL)

JSON response

Inferring Deeper Relationships between CustomersFind relationships between customers because it is not possible to accurately capture all relationships

between customers (as many customers may not even choose to declare the same).Find customers who share a common addressFind customers who have common nominee(s)Customers who have standing instructions to pay one account from anotherCustomers who are directors / promoters of the same companiesCustomers who share the same relationship with another customer (e.g., if Customer A is Customer B’s guarantor and Customer C’s guarantor then not only are Customer A and B related, and A and C related, the system must establish the relationship between B and C,

Aggregate network-level analyticsLocate customer’s with more than 10 common customers in their transaction networkDiscover customer networks which have high volume of within-network transactions in a month

Customer A

Customer A

XYZ Co.promotes Director

Community + Code Data Clients

DataWorksfamily

Web/Mobile Data

Social Public

Enterprise Data IoT

IBM Offers a User-Centric Data Services Platform

BusinessAnalyst

Tele/Web Agent

Data Scientist

Store, Provision, Govern User-CentricityAdvanced Analytics

Wealth Advisor

Compliance Officer

Thank You lvsubram@in.ibm.com