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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
CUST
OM
ER
LIFE
CYCL
E
• 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
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
T TO
DO
?HO
W IS
IT D
ON
E?
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
FORM
CAN
NAT
IVEL
Y CO
NN
ECT
AND
ACQ
UIR
E DA
TA F
ROM
…
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
ANAL
YTIC
S &
M
ODE
L LA
YER
Visualization & Dashboard
Native Mobile App Digital Platforms
CON
SUM
PTIO
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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