Radix Analytics Pvt. Ltd.

At Radix Analytics, we specialize in providing advanced data analytics and business intelligence solutions tailored to drive success across various industries. Our services are designed to address specific industry challenges, ensuring operational efficiency and superior decision-making. Below are the sectors we serve and how our tailored solutions are revolutionizing their processes.

1. Banking, Financial Services, and Insurance (BFSI)

We empower BFSI organizations with predictive analytics, risk management tools, and fraud detection systems that enhance operational excellence and customer satisfaction.

2. Energy

Our energy solutions focus on improving operational efficiency through predictive maintenance, energy forecasting, and consumption analytics, leading to sustainable and cost-effective operations.

3. Logistics

We offer logistics analytics that streamline operations, optimize routes, reduce delivery times, and provide real-time visibility into the supply chain.

4. Oil and Gas

Radix Analytics provides solutions that help oil and gas companies manage assets, predict equipment failures, optimize exploration processes, and maintain safety standards.

5. Pricing and Revenue Management

Our expertise in dynamic pricing allows businesses to leverage real-time data, maximizing revenue while staying competitive.

6. Sales Management

With our sales management tools, organizations can drive revenue growth, enhance customer engagement, and improve sales forecasting.

7. Consumer Packaged Goods (CPG)

We assist CPG companies with market analytics, demand forecasting, and insights into consumer behavior to remain competitive in dynamic markets.

8. Government

We provide governments with actionable insights to optimize public services, manage resources, and ensure transparency and efficiency.

9. Manufacturing

Our manufacturing analytics increase production efficiency and minimize downtime through predictive maintenance and real-time data insights.

10. Port Management

We offer port authorities advanced analytics tools to optimize cargo movement, improve operations, and ensure safety in port management.

11. Retail

Retail businesses can benefit from our customer behavior insights, inventory management solutions, and predictive analytics to boost sales and enhance the shopping experience.

12. Shipping

Radix Analytics provides shipping companies with tools for route optimization, fuel management, and real-time tracking for cost-effective and timely deliveries.

13. E-Learning

We support e-learning platforms with data-driven solutions that enhance learning experiences, track student progress, and personalize content.

14. Inventory Management

Our predictive inventory management solutions minimize stockouts, reduce excess inventory, and optimize overall supply chain performance.

15. Mining

We help mining companies manage resources, enhance safety, and optimize production processes with our cutting-edge data analytics solutions.

16. Preventive Maintenance

Our predictive analytics tools enable organizations to implement preventive maintenance, reducing downtime and extending asset life.

17. Revenue Management

Our advanced revenue management solutions help companies optimize pricing strategies, improve financial forecasting, and maximize revenue.

18. Supply Chain

We provide supply chain analytics that improve logistics, reduce costs, and enhance overall efficiency with real-time visibility.

Why Choose Radix Analytics?

At Radix Analytics, we transform raw data into strategic assets. By offering industry-specific solutions and working closely with our clients, we provide tailored insights that help businesses thrive in today’s competitive landscape. Our customized analytics tools range from predictive maintenance to real-time data visualization, enabling organizations to make informed, data-driven decisions.

Unveiling Fuzzy Match: Revolutionizing Data Matching with Advanced Algorithms

In today's data-driven world, the ability to accurately match and analyze textual data is paramount. Whether it's for identifying relationships, detecting anomalies, or optimizing search results, the need for precise data matching solutions is more critical than ever. Enter Fuzzy Match – the cutting-edge platform set to revolutionize how we handle and interpret textual data.

What is Fuzzy Match?

Fuzzy Match is an innovative platform developed by Radix Analytics that leverages advanced algorithms and machine learning to provide unparalleled accuracy in data matching. Designed to handle even the most challenging datasets, Fuzzy Match ensures you can efficiently search, match, and analyze data with ease.

How Does Fuzzy Match Work?

Fuzzy Match employs a sophisticated methodology combining advanced text matching techniques with state-of-the-art machine learning algorithms. Here’s a glimpse into how it works: 

Data Upload: Users begin by uploading CSV or Excel files containing textual data to the Fuzzy Match platform.

Search Query Processing: When a search query is received, Fuzzy Match's machine learning models analyze the query to identify relevant patterns within the dataset.

Column Selection: Users can select specific columns on which they want to search, and the search text can be a combination of multiple columns.

Fuzzy Matching and Semantic Analysis: The platform intelligently compares the query against the selected columns of the uploaded data, accounting for variations in spelling, formatting, and semantics.

Results: Fuzzy Match delivers highly precise search results, even when dealing with diverse and inconsistently formatted datasets. 

Why Choose Fuzzy Match?

Fuzzy Match is not just another data matching tool; it’s a comprehensive solution designed to address the most complex data challenges. Here’s why you should consider Fuzzy Match: 

Accuracy: Our machine learning algorithms ensure you get the most accurate matches possible, saving you time and reducing errors. 

Efficiency: The platform is designed to handle large volumes of data quickly and efficiently, making it ideal for organizations of all sizes. 

User-Friendly: With an intuitive interface, Fuzzy Match is easy to use, even for those without a technical background.

Continuous Improvement: The platform continually refines its matching capabilities through feedback loops and iterative learning, ensuring it stays adaptable to evolving user needs and data structures.

Get Started with Fuzzy Match 

INTRODUCTION

Data ethics and privacy are critical considerations in the modern digital age where vast amounts of data are generated, collected, and processed. 

Data Ethics are the norms of behavior that promote appropriate judgments and accountability when acquiring, managing, or using data, with the goals of protecting civil liberties, minimizing risks to individuals and society, and maximizing the public good.

In the simplest terms, data refers to factual information, such as measurements or statistics, used as a basis for reasoning, discussion, or calculation.

DATA ETHICS ASPECTS

Responsible Data Use:

Organizations should use data responsibly, ensuring that data is collected and processed for legitimate and ethical purposes.

Clear guidelines and policies should be in place to govern how data is collected, stored, and used.

Informed Consent:

Individuals should be informed about how their data will be used and should provide explicit consent before their data is collected or processed.

Transparent communication regarding data practices is essential to building trust with users.

Data Ownership:

Data ownership refers to the concept of identifying the party or entity that has rights and control over a particular set of data. 

It involves understanding who has the authority to make decisions about how data is collected, stored, processed, and shared. The concept of data ownership becomes particularly important in various contexts, including business, legal, and ethical considerations.

Data Security:

Data security is a critical aspect of information technology that focuses on protecting digital data from unauthorized access, corruption, theft, or damage. Ensuring data security is essential to maintaining the confidentiality, integrity, and availability of sensitive information

CORE PRINCIPLES OF DATA SECURITY 

Encryption

Access Controls

Firewalls

Regular Audits and Monitoring

User Training and Awareness

backup and Recovery

Endpoint Security

DATA PRIVACY  

Data privacy refers to the protection of individuals' personal information and the right to control how their data is collected, used, shared, and stored.

With the increasing digitization of information and the prevalence of online services, data privacy has become a significant concern. 

Data privacy needs to be a top priority for businesses. Failure to comply with data privacy regulations can lead to big losses. Think legal action, steep financial penalties, and brand damage.

KEY ASPECTS OF DATA PRIVACY

Data Minimization:

Data minimization is a principle in data protection and privacy that advocates for limiting the collection and processing of personal data to only what is necessary for a specific purpose.

The idea is to reduce the amount of personal information organizations collect and retain, thereby minimizing the potential risks associated with data handling.

Collect only the data that is necessary for the intended purpose. Avoid collecting excessive or irrelevant information.

Purpose Limitation:

Data should be used only for the specific purpose for which it was collected. Avoid using data for unrelated purposes without obtaining additional consent.

User Empowerment:

Individuals should have control over their data. Providing tools and interfaces for users to manage their privacy settings empowers them to make informed decisions.

Regulatory Compliance:

Organizations should comply with relevant data protection and privacy regulations (e.g., GDPR, CCPA) and establish procedures to ensure ongoing compliance.

Data Impact Assessments:

Conducting privacy impact assessments helps organizations identify and mitigate potential privacy risks associated with data processing activities.

Data Transparency:

Transparent communication about data practices and policies helps build trust with users and enables them to make informed choices about sharing their data.

Employee Training:

Train employees on data privacy best practices, emphasizing the importance of safeguarding personal information and complying with privacy policies.

Regulatory Compliance:

 CREDIT SCORE

Credit score is a number representing the creditworthiness of an individual. 

Credit bureaus collect credit information of borrowing and repayment and sell it to creditors for a fee.

Credit score is primarily based on information sourced from a credit bureau.

There is no international credit score.  Lenders assesses credit worthiness specific to different country and region.

SCORING COMPONENTS – FINANCIAL AND DEMOGRAPHICS

Number of accounts Active / Inactive / Current / Default

Length of credit history

Credit mixMortgage, auto loan, credit card, personal loan

Repayment history

Age

Occupation Self employed / Full time / Half time / Unemployed / Retired

Years of employment 

Income

Residential status Rent / Ownership

SCORECARD DEVELOPMENT METHODS

Expert scorecard Human subject matter expert

Classical methods Generalized Linear Model (GLM)

ML models Random Forest, Boosting

Deep learning Neural network for classification

CASE STUDY 1

A company in the UK wanted a scorecard for its customers applying for auto loan.

The profile of the customers was sub prime.  

The vehicles were primarily used ones.  

The lender wanted to improve alignment between underwriter rules and the score.

They also wanted to compare two different bureau data in terms of quality and coverage.

Objective

Application scorecard for auto loans for subprime customers

Solution & Benefit

Customer classification using domain knowledge and statistical methods like decision tree and cluster analysis. 

Multiple scorecards each rescaled to have similar odds 

Scorecard as a linear function for easy integration with loan origination system

Revision of underwriting rule and corporate reporting system to de-duplicate variables across model & underwriting rule engine

Multiple scorecards superior to a single scorecard

Recommendation on underwriting ensured the exclusion of model variables from underwriting rule

Consistent decision from adaptation of a  mathematical model

CASE STUDY 2

A major Telecom company in Malaysia wanted to create a score for its customers.

The company was using a bureau score which, was developed by using a database of 1 million customers.

Telco default was disconnected/suspended 6 months ever.

A tailor-made scorecard for Telco customers was thought to have better discriminatory power.

Objective

A telco scorecard to replace bureau scores

Solution & Outcome

Characteristics selected from broad areas of auto finance, credit card, personal loan, months in arrears, check bounce, application/approval amount, etc. 

Developed scorecards using: 

1. Logistic Regression (LR)

2  Machine Learning (ML) methods 

Random Forest (RF)

Extreme Gradient Boosting (XGB)

Interpretation of ML models were made possible by the use of Local Interpretable Model-Agnostic Explanation (LIME).

Logistic and ML models were developed of which XGB performance was superio

Outperformed bureau score

CASE STUDY 3

A company in Malaysia wanted to create a customized score for small-ticket loans.

The company used the bureau score which was developed using Logistic regression.

It was felt that a generic bureau score was not discriminating enough for this segment.

A tailor-made scorecard and ML model for evaluating credit risk were required.

Objective

A scorecard with bureau data for small-ticket personal loan

Solution & Benefit

Characteristics selected primarily from the history of credit card and personal finance of which variables from payment and MOB were the most important variables

Developed scorecards using: 

Logistic Regression (LR)

Extreme Gradient Boosting (XGB)

Interpretation of ML models were made possible by the use of Local Interpretable Model-Agnostic Explanation (LIME).

Satisfactory LR and ML scoresboth aided in decision-making

Model worked for mid level loansensuring application of the same scorecard for a different segment

SMALL AND MEDIUM ENTERPRISES (SME)

Small and Medium Enterprises (SME) play a major role in the economies of developing countries.

However, about half of formal SMEs do not have access to formal credit. Access to finance is the key constraint on SME growth compared to large firms.  

To address this, the World Bank has taken the initiative to support innovative finance and improved credit infrastructure.

This has resulted in generating increased level of fund for SMEs in the last decade.

SME LENDING

In the context of growth in SME financing, there is a greater need on the part of lending institutions to mitigate risk.

SME credit analysis produces a score indicating the probability of default of the borrowing entity.

Having a credit rating not only helps SMEs go beyond obtaining a loan but also provides leverage to negotiate with suppliers for procuring durables, equipment, and raw materials. 

SCORECARD DEVELOPMENT METHODS

Expert scorecard : Human subject matter expert

Classical methods : Generalized Linear Model (GLM)

ML models : Random Forest, Boosting

Deep learning : Neural network for classification

CASE STUDY 1

A Bank in Indonesia had a newly acquired SME portfolio.

The scoring model would be used for SME loan origination decisions

This was a key component in scaling up operations in accordance with Indonesian government directives.

Objective

Application scorecard to facilitate SME loan origination decisions

Solution

The history of default was not well established with sparse data since the SME portfolio was new. 

The bootstrap method was used to overcome the limitation of a small sample.

Business risk, financial risk, and moral risk were considered in the model.

Reject-inference was successfully employed.

High model performance achieved 

Benefits

Predictive modelling : replaced gut feel

Efficient process : from instant scoring

Consistent decision : from adopting a mathematical model

CASE STUDY 2

A lender in the UK finances a lease of office equipment to SME

Rapid depreciation of leased assets does not make them a good choice for collateral.

The lender used to cherry-pick customers who seldom went bad.

The finance company wanted to expand its customer base while mitigating risk.

Objective

A scorecard to replace rule driven underwriting for better screening

Solution

Various default criteria were tested.

Use of bureau data representing the status of SME (liquidation, insolvency, dissolution) to construct the default.

Analyzed the types of SME business groups and the quality of dealers.

Separate scorecards for full-account companies and micro entities

Checked the quality and coverage of two different bureau data to assess the indeterminates (Not high or low score so no clear indication of Good or Bad)

Benefits

Data-driven model : replaced rule-based model

Reduce underwrite workload : by restricting scrutiny to fewer applications which get mid-level scores and not extreme scores

Separate scorecard for micro entities : ensured these entities are not penalized for lack of accounts data

CASE STUDY 3

A multi-finance company in Indonesia provided loans to SMEs.

The company did not have a fully developed data warehouse and loan origination system for SMEs.

The bank had a policy of strict data security, whereby no data was to leave their server for analytics work.

Objectives

Multiple scorecards for various portfolios

Link the scorecard to the core banking system to produce an instant score at the time of application.

Solution

The data processing and modelling exercises were accomplished in-house with proprietary software for credit scoring. 

Separate scorecards were developed for four portfolios

In the absence of a core banking system, webservice was created to input data and receive instant score. 

A database was created to store newly entered application data.

Benefits

Instant scoring of new applications : improves efficiency in decision-making.

Process automation : ensured consistency, decreased manual work, and increased accuracy

 INTRODUCTION TO TIME SERIES ANALYSIS

Definition: Time Series Analysis (TSA) is a potent analytical tool for deciphering patterns and trends within time-ordered datasets.

Importance: Crucial in understanding sequences of data, be it financial, environmental, or any domain with time-dependent variables.

Objective: This presentation aims to guide you through the fundamental concepts, techniques, and applications of Time Series Analysis.

COMPONENTS OF TIME SERIES DATA

Trend: Represents the long-term movement in data, offering insights into overall direction (upward, downward, or stable).

Seasonality: Regular and predictable variations occurring at fixed intervals, helping identify patterns like monthly sales spikes or yearly weather trends.

Cyclical Patterns: Longer-term trends that might not follow a strict schedule, capturing economic cycles or other non-seasonal trends.

Residuals: Unpredictable and random fluctuations beyond the identified trend, seasonality, and cycles.

KEY TECHNIQUES IN TIME SERIES ANALYSIS

Descriptive Analysis:

Visualization: Line charts, bar graphs, or heatmaps to explore temporal patterns.

Summary Statistics: Mean, median, and standard deviation to provide a snapshot of central tendencies and variability.

Smoothing Techniques:

Moving Averages: Averages over a defined window, reducing noise and highlighting trends.

Exponential Smoothing: Assigns varying weights to different data points, emphasizing recent observations.

STATISTICAL MODELS

ARIMA Models:

Autoregressive (AR): Incorporates past values for predicting future outcomes.

Integrated (I): Deals with differencing to make data stationary.

Moving Average (MA): Considers past forecast errors to improve accuracy.

STL Decomposition:

Seasonal Decomposition: Separates data into trend, seasonality, and residuals, aiding in a more granular analysis.

MACHINE LEARNING APPROACHES

Utilization of machine learning algorithms such as Long Short-Term Memory (LSTM) networks for more complex and dynamic time series predictions.

Applications of Time Series Analysis:

Financial Forecasting: Predicting stock prices, currency exchange rates, and market trends.

Demand Forecasting: Anticipating product demand to optimize inventory and supply chain management.

Energy Consumption Prediction: Forecasting energy usage patterns to optimize resource allocation.

Healthcare Predictions: Monitoring patient vital signs, predicting disease outbreaks, and managing healthcare resources efficiently.

Climate and Weather Predictions: Analysing temperature patterns, precipitation, and other meteorological factors for accurate weather forecasts.

APPLICATIONS, CHALLENGES, AND CONCLUSION

Applications: Financial Forecasting, Demand Prediction, Healthcare Analytics, and Climate Monitoring.

Challenges and Considerations: Irregularities, missing data, model selection.

INTRODUCTION

In the ever-evolving landscape of technology, data analytics stands as a pivotal force, driving informed decision-making across industries. As we embrace the era of big data, artificial intelligence, and advanced computing, the future of data analytics promises to be both transformative and revolutionary. 

Let's embark on a journey into the realms of tomorrow's data analytics, exploring the trends, technologies, and possibilities that will shape the way we derive insights from data.

AI-POWERED ANALYTICS

The integration of artificial intelligence (AI) into data analytics is set to redefine the capabilities of predictive modeling and data interpretation.

Machine learning algorithms will play a crucial role in automating data analysis, uncovering patterns, and providing real-time insights.

AI-driven analytics will enable businesses to make faster, more accurate decisions based on a deeper understanding of their data.

EDGE ANALYTICS    

The future of data analytics will witness a shift towards decentralized processing with the rise of edge analytics.

Edge computing allows data analysis to occur closer to the source, reducing latency and enabling real-time decision-making in scenarios such as IoT devices and smart sensors.

This trend will be particularly impactful in industries where instantaneous insights are critical, such as healthcare and manufacturing.

EXPONENTIAL GROWTH OF UNSTRUCTURED DATA

With the proliferation of multimedia, social media, and other unstructured data sources, the future of analytics will grapple with managing and extracting meaningful insights from vast and diverse datasets

Natural Language Processing (NLP) and advanced text analytics will become integral to deciphering the value hidden within unstructured data, providing a more comprehensive understanding of customer sentiments and market trends.

ETHICAL AND RESPONSIBLE DATA ANALYTICS

With increased public awareness about data privacy and ethics, the future of data analytics will prioritize responsible practices.

Ethical considerations in data collection, usage, and storage will become integral, requiring organizations to establish transparent and accountable data analytics frameworks.

AUGMENTED ANALYTICS

The rise of augmented analytics will empower business users with tools that automate data preparation, insight discovery, and sharing, reducing their reliance on data scientists.

Natural language interfaces and automated insights will make data analytics more accessible to a broader audience within organizations.

CONCLUSION

The future of data analytics is an exciting frontier where technological advancements and evolving trends promise to unlock unprecedented possibilities. 

As businesses and industries adapt to these changes, the journey towards data-driven decision-making will become more dynamic, intelligent, and ethical. 

Concepts and Applications

Introduction

In the current television landscape, adaptability is crucial for broadcasters aiming to maximize impact amidst changing viewer behavior's. Moving beyond conventional viewership metrics, TV Media Analytics offers deep insights into audience demographics and preferences.

Leveraging this knowledge, broadcasters can strategically place Ads and set rates for optimal impact. Our product(s) utilize AI/ML models to navigate the complexities of TV Media Analytics, empowering broadcasters to thrive in the ever-evolving world of television advertising.

How does Media Analytics work ?

Traditional media analytics involves the systematic analysis of television content and audience engagement to extract valuable insights.

It employs various tools and methodologies to monitor viewership data, demographic information, and content trends. 

By tracking metrics such as ratings, viewer demographics, and social media mentions, analysts can gauge the success of TV programs, advertising effectiveness, and audience preferences.

Background

Radix Analytics provides data science solutions to the media industry. 

Recent engagement of Radix with a well-known broadcaster is the successful implementation and maintenance of Radix’s proprietary software, Ad-Spot Optimizer (ASO), a web application built on the IBM ILOG DOC optimization platform. ASO integrates with EMSIS in real-time. It takes ROs, FPC and agreements as inputs and provides revenue-optimized allocation of spots to programs and placement and sequencing within breaks. The allocation process is completely automatic.

Radix has developed Ad-Revenue Optimizer (ARO) and customized it for a well-known broadcaster – Proposal Builder along with five other modules. Proposal Builder creates advertising deal proposals in real time while maximizing profit margins while using minimum FCT. 

Case Study – Ad Spot Optimizer

Issues & Objectives :

Generate in real time (typically a few minutes) the daily spot allocation plan which determines the program/breaks in which each spot will be aired.

Solution : 

Designed and developed software with the following features

Assured allocation at spot, brand, advertiser, deal level

Even the distribution for spots of different brands, products, clients when the rates are the same

Long-term even distribution of spots across day parts from each deal time band

Benefits :

Maximizes revenue (2-4% incremental gain)

Automates the spot allocation process

Respects FCT Caps

All allocation rules, such as the cap on the number of ads for the same brand in a program/break are satisfied.

Checks that all deal conditions are satisfied while allocating ads.

Case Study – Ad Revenue Optimizer

Issues & Objectives :

To automate and optimize advertisement inventory planning for the client, resulting in additional revenue gain. The solution focuses on the creation of proposals, the planning of ad inventory, and post-evaluation.

Solution :

Designed and developed a software serve clients evolving requirements like state-of-the-art inventory visualization, advertising on digital and mobile platforms, interactive content, and comprehensive sales management.

Benefits :

Improved inventory pricing.

Advertiser-specific price variation and demand-driven pricing of inventory.

Improved allocation of inventory. 

Planned inventory overfill to manage day-to-day demand (RO) variation & avoid wastage.

Reduced servicing issues and 

Reduced make goods effort.

Improved pipeline visibility 

Sales executives performance tracking 

Negotiations history tracking for future reference

Improved handling of Make goods 

Streamlined, accurate, and faster billing

Dashboarding & Reporting Tools

Inventory Visualizer

Assess inventory fill by value/volume

BARC-View

Analyse viewership trends

PowerBI APIs

Combined data from multiple sources for analysis and reporting

Downfall of Corporations

Companies can go out of business for poor management, wrong decisions, changing times or if they get caught in dishonest and illegal activities which are termed as corporate fraud. 

The fraudulent activities include falsified accounting, forged documents, share rigging, misrepresenting services or products. 

    The Enron (US) scandal 

from 2001 was a result of series of dubious accounting practices

    Lehman brothers bankruptcy 

in 2008 can be attributed to faulty decision and downturn of US real estate market

    Satyam Computer Services (India) fraud 

in 2009 was from falsification of revenue, margin and cash balances

    Theranos scandal (US)

in 2018 for audacious lies on product

Regulatory Bodies

When a company goes out of business the impact is disastrous to economy. Larger the size of the company, bigger is the blow.

Not only the stockholders experience loss of wealth, but also the employees find themselves in uncertain circumstances with possible loss of retirement benefits. 

To protect the interests of investors and prevent fraudulent practices, governments around the world formed regulatory bodies.

Case Study

An online platform provides a structured database with information of registered companies on finance, key personnel, business activities, litigation, news, and board members.

The beneficiaries of this data include NBFCs, private banks, mutual funds, and private equity funds.

With this rich repository of information, the company created an expert score mimicking a credit rating to keep vigil on the companies to avoid financial disaster. Any shock in the daily score would raise a flag if something is amiss. 

    To do

Calibrate expert score with ML methodologies

Automate day-to-day score generation

Modelling Methodology

Company default was defined based on various criteria, like company rating, willful default, company audit report order, and NCLT cases.

The model was trained on historical data spanning 4 years. 

ML models included Random Forest and Gradient Boosting. Shows good separation of defaulters.

Separate models for listed and unlisted companies to address the issue of lack of data for the unlisted.

Decision overlay on final scoring for fine tuning.

Objectives of Credit Monitoring

The objective of the Credit Monitoring and Default Prediction System is to provide an advanced and automated tool for assessing the credit risk of Listed and Unlisted Companies in a timely manner, empowering lenders and investors to make informed decisions. The system utilizes predictive technology to identify companies at risk of default well in advance, offering a 90 to 360 days response time for effective remedial measures.

Machine Learning Analysis: Developed machine learning modals to identify patterns, categorizing companies on a risk scale from very high to very low.

Default Prediction Accuracy: It achieves over 90% accuracy in predicting potential defaults, instilling confidence in decision-making.

Event Pattern Recognition: Identifies event patterns, converting them into a quantifiable Risk Score for nuanced risk assessment.

In-Depth Analysis: Provides detailed analyses of risk-triggering data, offering insights into specific risk factors.

Auto-Alert Mechanism: Automated alerts ensure timely updates on portfolio risk changes, enabling proactive risk management.

Audience: Designed for lenders, investors, and stakeholders seeking enhanced credit decision support.

Benefits

    Process automation 

All changes in data are readily reflected in the score appearing on the dashboard.

    Continuous monitoring

TRAVEL INDUSTRY

Almost 10% of global GDP can be attributed to travel and hospitality industry

Fraudsters are naturally attracted to a sector with high monetary value, rapid growth and large amount of online and digital transactions

Various travel frauds arise from fake travel agents, transaction with stolen cards and reselling in grey market, sham website, insurance fraud etc.

$5.7 trillion spent in travel and hospitality industry in 2021

TRAVEL INSURANCE FRAUD

Fabricated trip cancellation when the real reason is not covered by insurance

False claim for lost baggage

Exaggerating the value of items in allegedly lost baggage

Fictitious medical treatment supposedly received during a trip

~20% of travel claims are fraudulent

PREVENTION OF INSURANCE FRAUD

Each insurance claim is scrutinized one by one and if possible,  compare with the previous claim history of the insured. 

Artificial intelligence and machine learning tools are widely adopted which  are trained over lots of data to look for anomalies of potentially fraudulent activity patterns. The methodologies used are

Outlier detection

Classification

Clustering

They consider a variety of features that a human may miss

Case Study

A Singapore based travel insurance company used to manually scrutinize each claim to detect potentially fraudulent ones

The process involved pre specified rule as well as judgment

Suspicious claims were subject to more detailed investigation

The investigating practice involved many hours of manual work and inconsistence process

Objective

Focus on reduced number of claims and thus 

reduce the manual work in initial scrutiny

while significantly improving fraud detection

Challenges

Data included 77,445 claim records of which only 120 had been determined to be potentially fraudulent 

Identified potentially fraudulent claims are rare events (0.15%) and therefore hard to detect 

It was however expected that there could be a large number of undetected fake claims

Solution

A powerful machine learning algorithm

Each claim was scored reflecting the risk of fraud

Higher the score higher was the probability of the claim being fraudulent

Benefit

Process automation

Bypass manual scrutiny

Efficiency

Save time and effort

Increased accuracy

Eliminate human error

Cost saving

INTRODUCTION

Fraud analytics is all about detecting unusual transactions arising from fraud/bribery after the transaction is completed (detection) or before they occur (prevention).

Fraud detection and prevention relies on data mining and makes use of ML and deep learning algorithms in addition to traditional rule-based methods.

TYPES OF FRAUD

Banking & Finance:

Occurs from unintentional data breach, website cloning, use of phishing and malware, stolen cards etc.

Insurance:

False claim in travel insurance and medical insurance.

Retail:

Can arise from employees, vendors, customers, and hackers who unintentionally or deliberately manipulate transactions.

HOW DOES FRAUD ANALYTICS WORK?

Machine learning techniques and neural networks can be effectively used in analyzing fraud:

Various outlier detection techniques help identify the anomalies between usual and unusual events.

Unsupervised methods like clustering algorithms create homogeneous groups to discover fraud patterns.

Supervised models are trained on fraud and other events to develop a pattern which can predict fraudulent activities by producing fraud scores. These scores are based on attributes like transaction amount and time, IP address, etc., or reject payments among many others.

CHALLENGES OF FRAUD ANALYTICS

Fraudsters are constantly improvising their tactics, which requires the models to keep pace and evolve.

Stringent rules from complex models may result in too many false positives causing harassment to genuine people by blocking legitimate transactions.

FUTURE

Fraudsters will continue to find new ways to manipulate system causing distress to business entities as well as to end consumers. The trail of digital fingerprints presents a big opportunity.

A systematic plan to pull in the wealth of available data scattered across different systems into a central platform will help take a holistic approach to analytics.

A BEGINNER'S GUIDE TO LEVERAGE 

DATA FOR MARKET ANALYSIS

INTRODUCTION

Customer Segmentation is the process of dividing your customers into homogeneous groups. Demographic factors such as age, gender, location maybe used.

Market Analysis is the process of gathering and analyzing information about a target market. This information can include demographics, psychographics, buying behavior and competitive landscape

HOW DOES CUSTOMER SEGMENTATION AND MARKET ANALYSIS WORK?

First we need to cluster customers based on their characteristics. 

Market analysis can be performed on these clusters to identify most profitable segments and can develop marketing strategies.

These tools are powerful that can enhance market penetration.

TYPES OF CUSTOMER SEGMENTATION AND MARKET ANALYS

USES OF CUSTOMER SEGMENTATION AND MARKET ANALYSIS

Improving market campaigns

Better product development

Enhanced customer service

Increased revenue/profits

Improved decision making

Increased customer retention

Improved competitiveness

CHALLENGES OF CUSTOMER SEGMENTATION AND MARKET ANALYSIS

If the data is inaccurate or incomplete, the results will be unreliable

It can be time consuming and expensive, business with limited resources may find difficult to implement

Need to hire skilled consultants or use better software to implement this due to high complexity

FUTURE OF CUSTOMER SEGMENTATION AND MARKET ANALYSIS

AI is automating marketing and sales tasks, including customer segmentation by analyzing large data sets and creating messages.

Personalization is crucial as customers demand unique experiences and businesses that utilize market analysis can gain a competitive edge.

Predictive Analytics Concepts and Applications

INTRODUCTION

HOW DOES PREDICTIVE ANALYTICS WORK?

TYPES OF PREDICTIVE MODELS

SOME APPLICATIONS OF PREDICTIVE ANALYTIC

CHALLENGES OF PREDICTIVE ANALYTICS

WAY FORWARD

Data volume from various sources is increasing at a rapid rate providing valuable resource for model building

Increased advancement of AI and ML has positive impact on accuracy of models

Rise of explainable AI helps in transparency and exploitability

Machine Learning For Data Analytics

INTRODUCTION

HOW DOES MACHINE LEARNING WORK?

TYPES OF MACHINE LEARNING

USES OF ML IN ANALYTICS

Identify patterns in data

Make predictions about future events

Cluster data into groups

Reduce the dimensionality of data

Improve the accuracy of data models

CHALLENGES OF ML IN ANALYTICS

It can be difficult to find and prepare the data that is needed to train the algorithms.

Machine Learning algorithms can be computationally expensive to train and run.

Some of the algorithms are difficult to interpret even though they work well.

FUTURE OF MACHINE LEARNING IN ANALYTICS

Machine learning is a rapidly evolving field, and there are many new developments in this area.

As machine learning algorithms become more powerful and efficient, they will be used for a wider range of data analytics tasks.

 A BEGINNER’S GUIDE TO UNDERSTANDING 

&

LEVERAGING DATA FOR BUSINESS INSIGHTS

INTRODUCTION

What is Data Analytics?

Data Analytics is the process of  examining data to extract insights and  draw conclusions. It involves using  statistical and computational methods  to analyze data sets, and can be used  to identify patterns, trends, and  relationships. Data Analytics is used in  a wide variety of industries, including  finance, healthcare, and retail.

TYPES OF DATA ANALYTICS

There are three main types of Data  Analytics: Descriptive, Predictive,  and Prescriptive. Descriptive  Analytics focuses on summarizing  past data to gain insights into what  has happened. Predictive Analytics  uses statistical models to make  predictions about future events.

Prescriptive Analytics uses data and  algorithms to recommend actions to  optimize outcomes.

DATA COLLECTION

To perform Data Analytics, you  need data. There are many ways  to collect data, including surveys,  web analytics, and social media  monitoring. It’s important to  collect high-quality data that is  relevant to your business goals.

Data should be organized and  stored in a way that makes it easy  to analyze.

CHALLENGES OF DATA ANALYTICS

While Data Analytics can be  incredibly useful, it also  presents several challenges. These include data quality  issues, privacy concerns, and  data overload. It’s important  to address these challenges to  ensure that your data analysis  is accurate and effective.

CONCLUSION