Gaining Real-time Data Insights and Advanced Analytics for Financial

Sector

Ghulam Imaduddin

APAC Director of Solution Engineering

October 2017

Banking & Digital Transformation

2

The banking industry in Indonesia is undergoing a significant transformation driven by technology

What is Trending?

3

Internetof Things

The interconnection via the Internet of computing devices embedded in everyday objects, enabling them to send and receive data.

Big Data

It is not just about volume of the data that getting bigger. It’s also about the velocity (speed of incoming data) and variety

Artificial Intelligence

AI is transforming our world. A set of algorithm and science that can predict the future, exposing more opportunity for business

The Benefit

4

SmartDevice

Smart cash machine that can identify the user from the camera. It can make sure the identity to prevent fraud

BetterRiskManagement

Advancing risk analysis with richer information and longer period of data

Accelerating credit scoringprocess from days to hour for better customer experience

Advanced trade decisionby doing NLP and sentiment analysis from scrapped data. Keywords joined with tick data and processed by ML/DL algorithms to calculate risk scores on demand.

Internet of Things

Big Data

Artificial Intelligence

Going More Advance

5

Deep Learning for Mortgage Risk

https://arxiv.org/abs/1607.02470

2005

2008

2010

2012

Deep Neural Network20 Years Mortgage Data, 120 Million Loans

3.5 Billion Records, 2 TB Data300 Explanatory Features

Deep Learning for Trade Decisioning

Deep Neural Net on Limit Order Books1.5 years of equities data50 Terabytes of raw data

The Challenges

6

Complex Algorithm = Heavy Computation

Stagnant to decreasing performance

Analytics challenges faced by the US Army Intelligence

7

200 sources of streaming data producing 20B new records per day.

Requirements to do ad-hoc analysis with human-response time on hot data.

Reduce reliance on expensive racks of premium hardware.

Kinetica incubated as a massively parallel computational engine for US Army INSCOM

GPU Acceleration Overcomes Processing Bottlenecks

8

3,000+ cores per device in

many cases, versus 16 to 32

cores per typical CPU-based

device.

High performance

computing trend to

using GPU’s to solve

massive processing

challenges

GPU acceleration brings

high performance

compute to commodity

hardware

Parallel processing is

ideal for scanning entire

dataset & brute force

compute.

GPUs are designed around thousands of small, efficient cores that are well suited to

performing repeated similar instructions in parallel. This makes them well-suited to the

compute-intensive workloads required of large data sets.

GPU | Tale of Numbers

9

100x

75%

Performance

100x gains over traditional RDBMS / NoSQL / In-Mem Databases

Cores

Modern GPUs can consist of up to 3000+ cores compared to 32 in a CPU

Costs

75% reduction in infrastructure costs, licensing, staff, etc.

More with Less

Increase performance, throughput, capability while minimizing the costs to support the business

GPUs are designed around thousands of small,

efficient cores that are well suited to performing

repeated similar instructions in parallel. This

makes them well-suited to the compute-intensive

workloads required of large data sets

Performance Increase

Infrastructure Cost Savings

3000vs

32

GPU Database Technology: Features to be Considered

10

Product Maturity andEnterprise Readiness

Easiness of IntegrationWith Another Tools

Data Security, Integrity, and Persistence

In-DatabaseAnalytics

GeospatialSupport

Kinetica Architecture

11

ETL / STREAM PROCESSING

ON DEMAND SCALE OUT +

1TB MEM / 2 GPU CARDS

SQL

NativeAPIs

PA

RA

LLEL ING

EST

Geospatial WMS

Custom Connecto

rs

In-Database Processing

CUSTOM LOGIC BIDMach

ML

Libs

BI DASHBOARDS

BI / GIS / APPS

CUSTOM APPS & GEOSPATIAL

KINETICA ‘REVEAL’

STR

EAM

ING

DA

TAER

P /

CR

M /

TR

AN

SAC

TIO

NA

L D

ATA

UDFs

Risk Management with AI

12

BUSINESS OBJECTIVE

• Move counterparty risk analysis from batch/overnight to a streaming/real-time system for flexible real-time monitoring by traders, auditors, and management

NEW CAPABILITIES DELIVERED

• Ability to handle time-sensitive, compute-intensive risk computations to project years into the future across hundreds of variables

• In-database analytics to run custom XVA algorithms at scale with GPU’s massive parallelization

SOLUTION OVERVIEW

• Kinetica operates as a real-time risk modeling engine running on public cloud-based, Microsoft Azure GPU instances

• Turn-key solution with elasticity, security, ease-of-use, and faster time-to-value

Risk Management

13

STREAM

PROCESSING

ON DEMAND SCALE OUT

IN-DATABASE PROCESSING

MONITORING

Global Positions

Regional Positions

ACCOUNTABILITY

VaR Limits

PRE-TRADE

What-if Scenarios

RISK MANAGEMENT

P&L, Commissions

Sensitivities

Liquidity Risk

Counterparty Risk

Global / Regional

Heads

Desk Managers

Traders

Spot Prices,

Transactions,

Market Risk

Data

External

Transactional

Records

UDF Functions

POSITIONS

ENGINE

CALCULATIONS

PRICING MODELS

RISK

APPLICATIONS

More data available.

Data flowing faster. Many new types of users demanding

sophisticated real-time analysis.

Kinetica

Connectors

ANALYTICS

Trade Decisioning

14

STREAM

PROCESSING

ML L

IBS

ON DEMAND SCALE OUT

IN-DATABASE PROCESSING

APIs

SQL

SQL QUERY

FINANCE

APPLICATIONSFINANCIAL DATA

Tick, Transactional

NEW DATA

Social Media, Forums

Scraped data from blogs,

reports, web, forums…

NLP and sentiment analysis to

classify text and trigger

decisions.

Kinetica

Connector

s

UDF FunctionsKeywords joined with tick

data and processed by

ML/DL algorithms to

calculate risk scores on

demand.

ON DEMAND SCALE OUT +

1TB MEM / 2 GPU CARDS

Fraud Analysis

15

Real-Time Fraud Engines

ML

LIB

S

NativeAPIs

SQL

Fraud

FIN

AN

CIA

L

DA

TA

Cre

dit C

ard

, A

TM

Real-time Financial Data Streams

Key/Value lookups for pattern

analysis. Spatial analytics looking at

time and distance between

swipes/ATM transactions. Ability to

score against trained models.

UDF Functions

BIDMach

Deep Learning APIs for

pattern detection and

predictive analytics. SQL

queries for BI analysis.

ANALYTICS

SQL QUERY

FINANCEAPPLICATIONS

IN-DATABASE PROCESSING

www.kinetica.com

Take the Kinetica Challenge!Try it with your own data

Email: info@kinetica.com

Don’t forget to visit our insightful booth!

DON’T SEND A CPUTO DO A GPU’S JOB