WEALTH MANAGEMENT ANALYTICS

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Competitive DifferentiationNew Players entering the Industry

especially Fintech Companies who are disrupting the traditional model

Better Insights & TransparencyCustomers are demanding more

transparency in terms of the portfolio management and a holistic advice by the

Wealth Manager

New Generation of CustomersExpectations and Preferences of these

investors are very different than the investors of the earlier era

Technological DisruptionsData and Analytics has ushered in a new

disrupting the way WMs engage with customers, managing risk or unbiased non-

human interface for advice

Addressing Mid Tier PyramidRetail Investors expect same level of advice and recommendations of asset classes as

HNIs. Also to reach this segment, technology is required to keep costs lower

Digital Disruption Wealth Management Analytics – Why?

DESCRIPTIVE AND DIAGNOSTIC ANALYTICS FOR WEALTH MANAGEMENT INDUSTRYIntegrated View of the various performance and risk metrics for a Wealth Manager and the Investor

Portfolio RecommendationProvides an AI / ML based recommendation engine on an ideal portfolio for a Customer based on Risk Profile and

Demographic data

Portfolio Monitoring & ServicingProvides a real time view of the Portfolio against markets

and performance benchmarks

Portfolio RebalancingAI and ML based real time rebalancing recommendation

based on Returns, Risk and Tax

Customer OnboardingProvides a view on where the Customers are coming from and whom should we target

next

Customer RetentionUnderstand the

Customers who are leaving and predict the customers who

are leaving

Wealth Management Analytics – Descriptive to Predictive Insights

Descriptive:• Customer base

by profile segment

• Acquisition trend & its co-relation with Index, political scenario

Prescriptive• Reach lower end of

the pyramid through AI based recommendation

• Investor Profiling

Descriptive:• Map out the

right mix of Portfolio based on Demographics, Risk and Return

Prescriptive• Recommending the Right

Product or Service• Chatbots to handle

Customer Queries

Descriptive:• Understanding Asset

Allocation, Valuation or Holding Analysis

• Monitor Risk Metrics

Prescriptive• Gain insight on how the

valuation or returns for an investor based on statistical forecasts

Descriptive:• Understand the Tax

Payouts, Losses or Gains incurred on the Portfolio of the earlier Portfolio Rebalancing

Prescriptive• Recommend the right

rebalancing strategy to an investor based on the efficient tax planning or asset mix or risk profile

Prescriptive• Identify potential attriting

customers by understanding their transactions, communication etc. in advance to draw up retention strategies

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• Historical /Current allocation• Benchmark Comparison• Assets under Management

Asset Allocation & Management

• Equity /Bond Sectors (Large Cap, Mid Cap, Small Cap etc.)• Top 10 Bond/Equity Holdings, Regional Allocation• Benchmark Comparison

Sector /Region Allocation

• Holdings• Current Market Value, Average Cost Price, Accrued

Income• Duration, PE Ratio, Dividend Yield, YTM, Hedged Yield

Valuation

• Currency Allocation, Exchange Rates• Cash Flow Analysis• Dr/Cr Interest, Overdrafts

Cash /Currency Matrix

• Portfolio Performance Stats (Latest, 1Y, 5Y, Since Inception)

• Benchmark Comparison, Composite Performance• Asset /Sector /Region Performance and Attribution

Performance

• Tracking Error, Sharpe Ratio, Information Ratio• Volatility (SD), Turnover RatioRisk Metrics

Illustration of Investment Manager Insights

• Investment Objective, Risk Tolerance, Guidelines• Allocation Strategy• Benchmark Managed Against

Portfolio Strategy

• Holdings, Market Values, Cost Price, Bond /Dividend Yield• Asset / Sector /Regional /Currency AllocationPortfolio Valuation

• Buy/Sell Transactions• Corporate Actions, Management / Custody / Safekeeping Charges• Dividends / Coupons Received

Transaction History

• Portfolio Performance Stats (Latest, 1Y, 5Y, Since Inception)• XIRR• Benchmark Comparison• Asset /Sector / Region Performance and Attribution

Performance

• Income Received• Realised Gain/Loss• Unrealised Gain/Loss

Tax Reporting

• Market Review and Trade Rationale• Economic Outlook, Forecasts*• Portfolio positioning

Market Outlook

Illustration of Customer Insights

PREDICTIVE USE CASES FOR WEALTH MANAGEMENT COMPANIES

High Level use cases from Acquisition, Monitoring, Risk Management and Handling Attrition

Customer Onboarding

Portfolio Recommendation

Portfolio Monitoring & Servicing

Portfolio Rebalancing

Customer Retention

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• Reach to the Lower end of the Pyramid through Cost Effective Models like Social Media Profiling

• Focused Target Marketing through Statistical Modeling from a Campaign database

• Recommending the Right Product or Service to a Customer based on their Life Stage and Risk Profile through AI & ML

• Forecasting of Portfolio Value & Returns over the next year*

• Intelligent Rebalancing of the Portfolio through ML based on the ideal Portfolio for an investor

• Using ML in Reinvesting of Dividends or Loss Harvesting to plan tax efficiently

• Chatbots to handle Customer Queries, Recommendations based on their Transactions

• Recommending the Next Best Product to a Customer

• Understanding Customer Behavior and Transactions to predict a likely attrition

• A trained Model to provide real time offers to retain Customers

Kickstart the Journey through Analytical / Predictive Solutions available

USE CASE – CUSTOMER ONBOARDINGCREATION OF INVESTMENT PROFILES USING SOCIAL PROFILE DATAShows how Social Profile data of potential prospects can be used to target either for acquisition or cross sell a product / service

How does everything come together on KGfSL analytics

Your catchment audience is present in multiple social platforms

KGfSL Analytics has pre-built connectors to automatically pull data at any chosen frequency

KGfSL Analytics Platform for structured and un-structured

content

Definitions based applications

Advanced Statistical Algos

A single data and application platform for processing & insights

Visual insights. Machine consumable insights for micro-profiles

Micro-profile based model asset class allocation recommendations

There are different approaches to get to data – KGfSL supports all possibilities

Full Auth

Association Auth

No Auth

• Text blurbs • Sentiment• Word dominance• Profile text

analysis• Device preferences• Time of day

preference• Media Preference• Other

Txn, Portfolio, Service, Finance

data

• Micro-profile accuracy is a function of content• Institution has the flexibility to change model

asset class allocation dynamically

Internal Systems Social Platforms

API Based Pull from both forms of authentication

Logical schematic of the Solution

Internal Applications

Digital Venues where the Institution has presence

Twitter Facebook LinkedIn Instagram

Data pull from system

SINGLE CUSTOMER VIEW DATA RESPOSITORY

CRM Service

WebsiteAccounting

Micro Profile 1 Micro Profile 2 Micro Profile 3 Micro Profile N

Twitter

Taxonomy of sentiments, emotions and behavioral traits

INVESTMENT SPECTRUM

Aggressive Asset Class: Derivatives Defensive Asset Class: Fixed Income

Passive Asset Class: ETF

TEXT MINING SENTIMENT ANALYSIS

…MAP MICRO-PROFILES TO ASSET CLASS

COMBINATIONS

Institution’s Customers self authenticate

INSTITUTION’s AUTHENTICATION CUSTOMER SELF AUTHENTICATION

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Statistically segmenting visitors and their behavior leads to actionable insights

Look beyond your followers. Investigate hashtags, trends, your competitors…whatever you can think of

Running a social campaign? Analyze trends, keywords and blend your strategy

to insights

How are you faring vis a vis your competitors? Benchmark your activity with

your competitors

Solution Component Description

1The KGfSL Analytics platform has out of box capabilities to integrate with different data sources to acquire data for analytics. For social/digital presence either Institution can authenticate the data pull via APIs (SOLUTION A) or get customers to individually authenticate their own social media accounts for API based data pull (SOLUTION B)

2The data pulled from different data systems are commingled in a single analytical repository within KGfSL’s platform for downstream analytics using definitions and advanced statistical models involving text mining and analytics on unstructured content

3Working closely with the Institution a detailed taxonomy structure will be created to associate the sentiments and other behavioral traits mined from the unstructured data.

Using the taxonomy layer on the data acquired in Step 1, micro-profiles of the customer base will be created. We can use supervised clustering models also to segment customers into defined and non-overlapping customer traits. Each segment shall have a micro-profile associated and graded across the spectrum of “AGGRESSIVE” to “DEFENSIVE”

The investment risk spectrum – starting from ”AGGRESSIVE” at one end to “DEFENSIVE” at the other - will be broken down in terms of asset classed associated. ”N” number of model portfolios can be created and mapped to micro-profiles created in Step 4

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USE CASE – PORTFOLIO REBALANCINGTHROUGH PREDICTIVE MODELS

Shows how Statistical Models can be leveraged to recommend an unbiased Rebalancing Strategy based on Risk Profile, Customer Life Stage, Investment Objectives etc.

Portfolio Rebalancing through Machine Learning

Map the Customers Life Stage to Risk Profiles and Build a Historical Model to understand the Returns against such Portfolio Allocations

Real Time execution of Rebalancing Models;• Calendar or Time Based • Customer Life Stage Based• Tax Planning Based• Portfolio Drift Based• Event Based

WHA

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Track earlier Rebalancing Strategies to plot the Returns across Customer Profiles to create the right mix of a Portfolio Rebalancing

Descriptive Analytics Layer;• Risk Return Analytics across

various Portfolio Allocations mapped to Customer Risk & Demographic Profiles relative to a Target Asset Allocation over a particular time horizon

• Helps in understanding How often, How much and How far?

Regular monitoring of Rebalanced portfolios against target Asset Allocation and Risk Return Benchmarks

Real Time Monitoring through Analytics and Intelligent Alerts;• Analyze the Standard Deviations

of the Portfolio to see the effectiveness of the Rebalancing Strategy

• Ensure the maintenance of optimum rebalancing of the portfolio

Lower Transactional CostsA ML Predictive Model can ensure that the overall transaction costs are kept low and at the same time rebalance the portfolio keeping the underlying risk return

Customer RetentionGood Rebalancing Model helps in retaining Customers as they see growth in their returns and efficient tax planning

Keeping FocusRegular monitoring of the asset allocation ensure the focus remains on the ideal target asset allocation

Risk ControlHelps in paring back Outperforming

Asset classes or enhancing allocation to Underperforming Asset Classes to

preserve risk characteristics as well as enhance returns

Enhanced Risk ReturnGood and Unbiased or Non Emotional

Rebalancing Model can enhance the returns and managing the risk at

appropriate levels

Portfolio Rebalancing through Machine Learning - Benefits

ANNEXURE – DETAILS ON PREDICTIVE MODELS ACROSS LIFE CYCLE

Life Cycle and Analytical Solution Analytical Solution Description Business Outcome

Customer Onboarding

Reach to the Lower end of the Pyramid

• Using Artificial Intelligence (AI) and Machine Learning (ML) to provide financial / investment advise to a set of customers who were earlier not under the ambit of Wealth Management

• Real Time Customer Profiling through AI & ML

• Larger Customer reach improving Revenue and Profitability

Portfolio Recommendation

Better Investment Planning

• Using AI and ML to remove the biasness of investment recommendation

• Provide better Financial Planning Solutions to Customers through an automated way of understanding their financial accounts (understand their Savings and Spend patterns)

• Intelligent Rebalancing a Portfolio through ML which provides recommendations or handles sell / buy an asset class based on the ideal Portfolio for an investor

• Standard and Unified experience across Customers

• Better Tax Planning and Compliance for a Customer

• Improved Returns to a Customer

Portfolio Recommendation

Creation of investment profiles for individuals based on their social media footprint and other digital touchpoints of the Enterprise

• Investors segmentation and risk profiling to create an individual specific investment profile matching risk –return expectation and economic behavior.

• ML based Self-learning algorithm to profile investors factoring latest information.

• Optimize customer returns considering investment profile

• Increase depth of investors relationship thereby improving Net Relationship Value for investors

Kickstart the Journey through Analytical / Predictive Solutions available

Life Cycle and Analytical Solution Analytical Solution Description Business Outcome

Portfolio Monitoring & Servicing

Recommending / Selling the Right Product or Service to a Customer based on their Life Stage

• The Model analyzes Customer Demographics like Age, Employment Status, their Transaction behavior and push a recommendation to the Customer on their preferred channel of a Product or Service that they may be interested

• Build a life long relationship with the Customer

• Increases the chance of the Customer subscribing to the Product if recommended correctly

Portfolio Rebalancing

Recommend a rebalance strategy based on various types of rebalancing strategies

• Using AI in Reinvesting of Dividends or Loss Harvesting to plan tax efficiently

• Using ML Rebalancing Models to ensure Target Asset Allocation and Risk Return is maintained for a Customer

• Improved Rebalancing Strategy by removing emotions in rebalancing

Customer Retention

Chatbots to handle Customer Queries, Recommendationsbased on their Transactions (recommending investment Products or Other Products / Services)

• The Chatbots will initially be fed with various queries, complaints etc. and the outcome of the historical data. A ML model will then be run on this data to train and then tested on another sample data set

• Automation to reduce the dependency on Human Resources and improve failure rates

Kickstart the Journey through Analytical / Predictive Solutions available

CONSUMPTION

VISUALIZATION

EMBEDS EXPORT

Data Integration Layer (Portal, Transaction Systems, Market Data etc.

API

NANOMART

PREDICTIVE ENGINE

Recommendation

Onboarding Cross SellModels

ChurnRebalancing OthersModels

PREDICTIVE ANALYTICS WORKBENCH

REGRESSION SEGMENTATIONWHAT-

IFCHURN

Customer Analytics

Operational Analytics

Agents Analytics

Analytical Solutions

ANALYTICS PLATFORM

Bank’s Portal

Institution Users

API to embed required analytics / bots onto the

Portal

Dataflow for the Analytics

ANNEXURE – ANALYTICS PLATFORM

Predictive AnalyticsModel Workbench with “R”

Libraries integrated for Model Building and Execution

AI Based BotsThese bots monitor your

enterprise information network for analytic insights and pop them up

for you. Thus unleashing automation

Data WarehouseBuild Virtual Data warehouses using

the underlying neural metadata framework. Integrated Data

Preparation, Data Definition, Data Visualization Layer

Business IntelligenceUnderstand not only What

happened, but Why it happened and What is going to happen on the same interactive visualization layer

Visual Full Stack Analytics Platform

What is the Analytics Platform?

What differentiates the platform from any other analytics tool is the ability to ingest data from varied sources including external curated & unstructured data and provide comprehensive BI/Analytics, along with Predictive & Cognitive capabilities which can be

consumed in multiple ways i.e. machine to human & machine to machine

Single Analytic Platform for end to end analytic needs

Oracle SQL Server

MySQL

PostgreSQL

MongoDB

Data from relational databases

Data from Columnar databases

Data from NoSQL databases

Data from HDFS Other Data Sources

PLAT

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Create on-demand small yet interconnected datasets to analyze. Mix data from multiple sources

Unified platform for BI, Attributive and Predictive Analytics Platform. Integrated with “R” OR can invoke an executable built in SAS

Ensemble of statistical models run and outputs visualized on the platform or outputted through APIs

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Visualization & Dashboard

Native Mobile App Digital Platforms

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Metadata Metadata Metadata based REST API

JSON

Files Files

Files

…and many more

Operational Systems

Metadata based REST API

Data Acquisition Adapters

Benefits of a single stack platform: End-to-end analytics platform with everything you need to prepare, analyze and visualize complex data, eliminating the need to use a hodgepodge of tools. This cuts complexity, costs and maintenance overheads. Simplicity of the platform empowers business users create, consume and share analytical insights in decision making process

Descriptive Diagnostic Predictive Prescriptive

What to change

Automated data ingestion , interlinked storage, processing to Business Intelligence Applications

What will happen

Fully integrated predictive modeling framework on both structured and unstructured data

API based analytics-aware service bots for robo-actions

What Happened Why it happened

Single click root cause analysis for anomalous trends

World’s only platform that integrates DW, BI, Predictive and Prescriptive Analytics in a single visual stack

Access Analytics across Browser, Mobiles (through a native App) for “anywhere-access”

Intelligent Alerts and AI Service Bots

Define complex conditions and let platform’s intelligent alerts notify you when required…

The platform has fully capable AI based “analytics-aware Service bots”These bots monitor your enterprise information network for analytic insights and pop them up for you

You can ask the Bots questions in simple English

Platform’s service-bots can trigger downstream events using the platform’s native APIs. Your enterprise can unleash automation like never before leveraging platform’s service-bots

THANK YOU

Reach out to us for any additional queries or clarifications