AI IN FINANCIAL SERVICES

FIAZ MOHAMEDINTEL AI PRODUCTS GROUP

ANALYTICS NEEDS AI

HindsightWhat Happened

InsightWhat Happened and Why

What Will Happen, When, and Why

Simulation-Driven Analysis and Decision-Making

Self-Learning and Completely Automated Enterprise

Mature Data Lake

Computerized Human Thought Simulation and Actions Towards Autonomic Enterprise

DescriptiveAnalytics

DiagnosticAnalytics

PredictiveAnalytics

PrescriptiveAnalytics

CognitiveAnalytics

Advanced Analytics

Operational Analytics

To

day

Em

erg

ing AI

is a vital tool for

reaching higher

maturity & scale

Foresight

VARIETY OF AI APPROACHES

CLASSICAL ML DEEP LEARNING REASONING EMERGING

Statistical problems, recommendation

engines, transparency

requirement, etc.

Image/speech recognition, natural

language processing, pattern

recognition/detection, etc.

Multivariate supply chain probe, full database fraud

detection, whole CRM churn analysis, etc.

AI research: ‘sequence alignment’

in computational biology, ‘binary

neural network based inferencing’, etc.

EX

AM

PLES

CLASSICAL ML VS. DEEP LEARNING

Random Forest

Support Vector Machines

Regression

Naïve Bayes

Hidden Markov

K-Means Clustering

Ensemble Methods

More…

CLASSICAL ML

Using optimized functions or algorithms to extract insights from data

TrainingData*

Inference, Clustering, orClassification

New Data*

DEEP LEARNING

Using massive labeled data sets to train deep (neural) graphs that can make inferences about

new data

Step 1: Training

Use massive labeled dataset (e.g. 10M tagged images) to iteratively adjust weighting of neural network connections

Step 2: Inference

Form inference about new input data (e.g. a photo) using trained neural network

Hours to Days in Cloud

Real-Time at Edge/Cloud

New Data

Untrained Trained

Algorithms

CNN,RNN,RBM..

*Note: not all classic machine learning functions require training

REASONING SYSTEMS

MEMORY BASED

Using associations between concepts from multiple data types to make sense of

complex situations

Flexibility to handle ALL data types at once

Incorporate new data in real-time

Transparent and explainable

e.g. under what system conditions should I perform preventive maintenance to avoid a failure?

LOGIC BASED

Using a rule-based reasoning engine, usually hand-created or maintained, to

perform logical inferencing steps

Explicit encoding of knowledge

Repeatable, reversible, deterministic

Transparent and explainable

e.g. should I maintain or alter my equity portfolio given my risk profile?

INTEL® NERVANA™ PLATFORM

A full stack, user-friendly & turnkey system

that enables businesses to develop and

deploy high-accuracy AI solutions in record

time:

-or-

Compress the Development Cycle

PureAcceleration

Benefits Beyondthe Box

*Other names and brands may be claimed as the property of others.

DEEP LEARNING IN PRACTICE

TABULAR DATA ANALYSIS

IMAGE CLASSIFICATION

Arjun

DOCUMENT ANALYSIS

VIDEO ACTIVITY DETECTION

Baby Crawling 0.91

TIME SERIES DATA

SPEECH ANALYSIS

INTELLIGENT KNOWLEDGE MANAGEMENT

Highly diversified financial services and asset management firm with >$750B

Deep learning-based clustering of documents by topic

Faster and more accurate insights by reducing the number of documents needed to drive decisions

Reduce time to insight for portfolio managers

Knowledge management system based on DL natural language processing engine to:

• Ingest vast stores of unstructured data

• Predict topical relevance of paragraphs

• Recommend relevant articles with Q&A system

Client

Challenge

Solution

Advantages

INTELLIGENT ORDER MATCHING

Global financial services and asset management firm

• Streamlining of the trade execution process by automatically assessing quality of prices

• Learnings can drive better trading strategies and increased profitability

In trading system, quotes are placed by counterparties for instruments at different prices. The prices on the system are indicative and not firm and this results in different possible outcomes and different liquidity.

DL system to predict the probability of fixed income orders clearing at a user specified price level and trading partner• Model was trained on historical trading data • Allowed the client to select a counterparty and a price that had the

highest probability of being filled

Client

Challenge

Solution

Advantages

ORDER BOOK SEARCH AND PREDICTION

Leading U.S. equity marketplace

• More accurate matches than non-deep learning approaches

• Enables new use cases for fraud detection, anomaly detection, and other future intelligent applications

Drive future investment activity based upon similar historical data patterns

DL system to: • Ingest public order book data and

automatically learn patterns of activity• Enable search queries for similar

historical patterns

Client

Challenge

Solution

Advantages

Limit order book search

THANK YOU