ACCENTUREANALYTICS BOT

FEBRUARY 2018

WHY WE NEED ANALYTICS BOT?THE FINANCIAL SERVICE INDUSTRY IS CHANGING, FACING A NEW GENERATION OF USER, CLIENTS AND A NEW TECHNOLOGY LANDSCAPE

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1. The last decade has seen significant growth of web and mobile applications over the last two decades with users are interacting with banks more and more through their smartphones

2. Understanding large amounts of information, and using data in support of business strategy and goals is critical to the survival of traditional FS firms

22%

Mobile ATM Branch Phone Banking

% User initiated transaction by channel

2007

2017

The FS industry as a whole is falling behind other industries in its evolution towards a Digital Economy

10%20% 49%

80% 10% 8% 2

The Web Search Engine Google MapReduce Apache Hadoop New Analytics Methods Big Data

1996 2004 2005 Today2010 - 2014

WHERE IS FS FALLING BEHIND

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MOBILE APPSCHATBOTS IMPROVE USER EXPERIENCE

With more complex scenarios there is value by using chatbots to improve the user experience.

Because people have not always interacted with systems, chat bots built to respond to user queries using natural language (text or voice) and natural modes of human communication (text or voice) to engage in a dialogue and get responses, improve user experience through conversations rather than clicks and scrolls.

Majority of active user interactions on web/mobile apps are simple transactions such as checking balances, transactions or moving money etc.

WHY HOW1. Familiar: Messaging is a

familiar UI2. Fast: Seconds to send

text, voice and images3. Multi-channel: SMS,

messenger, online text, voice

1. Knowledge of data and customer Interactions, processes, rules and workflows within the business

2. Provide High Utility - do more than search results

3. Hold contextual, relevant conversations and speak the language of the business they server

BIG DATA ANALYTICS AND THE 5 V’SANALYTICS' VALUE

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With the explosion of digital data, there is now more information available to organizations about their businesses but there are challenges in leveraging this information.

Businesses must be able to use Big Data to enable analytics for better decisions and identifying actions to support

banks’ strategy, reduce cost, drive revenues, improve control, deliver excellent services to customers and create value for the organization

• Insights and analytics

Data Derivation and Standardization

High Value Analytics

Data Acquisition and Access

Valu

e

3.VelocityFast data streams

2.VarietyData source +

types

1.VolumeScale of Data

4.VeracityInaccuracy/uncertainty

Analytics

5.Value

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WHAT DOES IT DO

IMPORTANCEWHAT IS AccentureBot GOOD AT

Visual Analytics Chatbot that communicates with users through messaging apps, chat windows or voice using natural language.

Business Context: The Analytics powered Bot takes on the work of analyzing and interpreting large amounts of data to answer business

questions based on related transaction, customer and other enterprise data to support decision making.

The solution can also serve multiple functions within the organization to help firms prevent cost leakage, save on unwanted expenses related to

their resources efforts, increase team productivity and improve decision making and performance over a period of time.

Convenience: Multi channel - Accessible on any device

Future: Visual analytics leveraging emerging AI techniques

Automate: Reduce data exploration time, effort and resources

• Visualizing large volumes of data• Querying complex scenarios• Familiar interface for mobile experiences

People Process

Technology

Process• Natural Language

Processing• Changing the way people

interact with data

Technology• Mobile Visual Analytics• Multi-channel accessibility

People• User Experience• Human-Centered

decision making

support by AI

MORE THAN TECHNOLOGY… HOLISTIC ANALYTICS

HOLISTIC ANALYTICS APPLICATION USER INTERACTION

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Data Lake

I want to know the average credit score for the first

quarter of the year

People

‘Visual Analytics in Natural Language’

AI

AIP Analytics Engine

Chatbot

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1 BOT, 3 EXPERIENCES…LIQUID VISUAL ANALYTICS

There are 2 properties…

1 2 3Mobile Desktop Augmented/Virtual Reality

HOW THE ANALYTICS BOT WORKS

Browser

Azure Bot Service

Azure Identity validation

Language Understanding

(LUIS)

Microsoft Azure Platform

URL +predefined parameters

Text Analytics API

Speech API

Entities extraction

Parameter and Value

Instructions

Front End/UI

User make the request through the browser.

Speech to text

SPEECH AND TEXT PROCESSING

“Total of Mortgage loans in Florida in 2000”

Speech API

Text“Total of Mortgage loans in Florida in 2000”

Language Understanding

(LUIS)

Text Analytics API

“Total of Mortgage loans in Florida in 2000”

Turns speech to text

Text Analytics API search for key words (entities) on the text.

LUIS determines the correct action (intent) to do, identify entities and creates a JSON with these parameters.

{ "query": “Total of Mortgage loans in Florida in 2000”,{ "intent": “TotalMortgages", "score": 0.9887482},"entities": [ { "entity": “Mortgage loans", "type": “measure"},{ "entity": “Florida", "type": “state"},{ "entity": “2000", "type": “year"},] }

Bot App

URL + predefined parameters

Bot service transform the JSON object into an URL.

The result is a URL with predefined parameters

https://qlikserver/mortgageloans.html?state=Florida&year=2000

Teach LUIS

Utterance list_ Sum of credit Amount_ Total of mortgage loans_ Total Sum of mortgage loans_ Get the total mortgages credits_ Get Fanny Mae Credits_ Visualize total Credits in USD

SPEECH AND TEXT PROCESSING EXAMPLE

+

Re

qu

est

Data

Request & Process DataBOT requestsfrom identifiedintent (actions)and entities.

Extensions

BOTEmbedded

Functional QlikVisualization

VISUALIZATION PROCESSING

QlikAPIs

https://qlikserver/mortgageloans.html?state=Florida&year=2000

Through Javascript, parameters

are extracted from URL

dict = [

{key:”state”,

value:”Florida”},

{key:”year”

value:2000}

]

Values are passed to Qlik

Sense Mashup and it

integrate the values with

togleSelect() filter, Ex.

app.field(key). toggleSelect.(value)

.Through

Javascript,

parameters are

extracted from

URL

Qlik Mashup get the data from

the correct Qlik Sense app

and it returns it.

It is visualized on the browser

through HTML and CSS.

Finally, the visualization is

displayed according what the

user requested inside the Chat

bot.

BOT CALL

VISUALIZATION PROCESSING EXAMPLE