Cookie Preference Centre

Your Privacy
Strictly Necessary Cookies
Performance Cookies
Functional Cookies
Targeting Cookies

Your Privacy

When you visit any web site, it may store or retrieve information on your browser, mostly in the form of cookies. This information might be about you, your preferences, your device or used to make the site work as you expect it to. The information does not usually identify you directly, but it can give you a more personalized web experience. You can choose not to allow some types of cookies. Click on the different category headings to find out more and change our default settings. However, you should know that blocking some types of cookies may impact your experience on the site and the services we are able to offer.

Strictly Necessary Cookies

These cookies are necessary for the website to function and cannot be switched off in our systems. They are usually only set in response to actions made by you which amount to a request for services, such as setting your privacy preferences, logging in or filling in forms. You can set your browser to block or alert you about these cookies, but some parts of the site may not work then.

Cookies used

Performance Cookies

These cookies allow us to count visits and traffic sources, so we can measure and improve the performance of our site. They help us know which pages are the most and least popular and see how visitors move around the site. All information these cookies collect is aggregated and therefore anonymous. If you do not allow these cookies, we will not know when you have visited our site.

Cookies used

Google Analytics

Functional Cookies

These cookies allow the provision of enhance functionality and personalization, such as videos and live chats. They may be set by us or by third party providers whose services we have added to our pages. If you do not allow these cookies, then some or all of these functionalities may not function properly.

Cookies used




Targeting Cookies

These cookies are set through our site by our advertising partners. They may be used by those companies to build a profile of your interests and show you relevant ads on other sites. They work by uniquely identifying your browser and device. If you do not allow these cookies, you will not experience our targeted advertising across different websites.

Cookies used


This site uses cookies and other tracking technologies to assist with navigation and your ability to provide feedback, analyse your use of our products and services, assist with our promotional and marketing efforts, and provide content from third parties


Here are some suggested Connections for you! - Log in to start networking.

Mahmoud Matar
VP Sales
Irman Syaiful Qodri
Officer II B2C Loyalty
Sri Sumari
GM Health Care Service
Işık Tazeler
Chief Growth Officer

Sunil Kappal - Blog


As per the largest market research firm MarketsandMarkets the speech analytics industry will grow to USD 1.60 billion by 2020 at a Compound Annual Growth Rate (CAGR) of 22% from 2015 to 2020. Today the omnichannel world consists of voice, email, chat, social channels, and surveys, and each channel has its own importance.

Therefore, it becomes inevitable for any customer centric organization to ignore the information that can be glean out of these customer interactions.

This article talks about some cutting edge usage of Speech Analytics output coupled up with a computerized mathematical technique that allows organizations to account for risk which is called as Monte Carlo simulation. For the purposes of this article I will be focusing on the healthcare industry which has reported (The Economist May,31,2014) a staggering $275 billion swindle.

To use this technique (Monte Carlo simulation) in conjunction with the Speech Analytics output we will use the “Stochastic Model” for the simulation which involves probability or randomness.

Application of Monte Carlo Simulation to identify probability of fraud by Service Providers

The expected output of this simulation is to identify the likelihood of a fraudulent activity based on the key customer interaction that indicates potential “Fraud Outcomes”.

Identifying the fraudulent interactions


As we know that speech analytics allows its users to query the media files to identify the emerging topics. The above scenarios can be created within any speech analytics application. The user can also utilize the provider related metadata (additional information about a particular customer interaction) to understand the interaction distribution of the above scenarios by a particular provider.

How it works? Creating the Model

Scenario 1 + Scenario 2 + Scenario 3 + Scenario 4 + Scenario 5 = Fraud?

Let’s say we have over a million customer interactions with a combination of 5 scenarios (refer to the scenario grid), and we arbitrarily identify the interactions to decide if that interaction has a high likelihood of being a fraudulent scenario. No two scenarios will have precisely the same number of fraud manifestations. However, if we have an idea of the range of occurrences for each situation, then we can create a Monte Carlo simulation to better understand the probability of a fraud scenario.

The image below shows the simulation that I created in Excel that illustrates how the model was created using 1000 fraud simulations where each simulation is equally likely to happen.


The above simulation was done for multiple providers based on their fraud scenario % (it will be advisable to pick the outliers by keeping the fraud indicator scenario % in mind). Once the simulation for the top provider were created. I was able to showcase the providers who are prone to get into fraud related discussions with their customers.


 Note: Above frequency graphs are based on the Monte Carlo Simulation that gives a probabilistic perspective for the Fraud Indicator conversations that might lead to an actual fraud incident. The above outputs are based on the 1000 simulations where each simulation is equally likely to happen.

By looking at the above results one can easily isolate those providers or scenarios that can results in a potential fraud incident before it happens and mitigate a potential risk to the consumer, the brand and the overall reputation of any healthcare service provider.

Author: Sunil Kappal - Senior Analytics Manager

Publish Date: January 25, 2017 12:31 PM

Common Data Mining Techniques: Key to Detecting and Preventing Fraud

In my past article "Detecting Healthcare Fraud Using Speech and Data Analytics" I have described at length about the two most robust and common techniques used to identify and predict fraud. However, this article focuses purely on the most common Data Mining method CRISP-DM "Cross Industry Standard for Data Mining Process" that can help organizations to mine the data using some common techniques to identify fraud.

Before we proceed we should understand what is Data Mining?

“The process of discovering meaningful new relationships, patterns and trends by sifting through data using pattern recognition technologies as well as statistical and mathematical techniques.” – Gartner Group

Let’s look at some very common and basic fraud detecting techniques used based on the business motivation of either:

  1. Predicting or classifying a fraud
  2. Grouping or finding affinities/associations


  1. Techniques used to predict or classify a fraud
  • Regression algorithms: These algorithms predict a numeric outcome. Most commonly used algorithms are:
    1. Neural Networks
    2. Regression
    3. General Linear Modeling
  • Classification algorithms: These algorithms predict symbolic outcomes. Most commonly used algorithms are:
  1. CART
  2. Logistic Regression


  1. Techniques used to group and associate fraudulent transactions/events
    • Group and find association algorithms: K-means, Factor Analysis are amongst the most common and popular clustering and grouping algorithms used to detect fraud.
    • Association algorithms: Apriori algorithm is one of the most popular association algorithm used in the healthcare industry for frequent item set mining and association rule learning over the transaction database.

Use Case – 1: Regression

  • Predict the expected claim value, compare it with the acutal value of the submitted claim.
  • Cases falling outside the expected range should undergo rigorous scrutiny.

Use Case – 2: Decision Trees

  • Create characteristicts or provider profiles with fraudulent behaviors and indicators.
  • Extract cases meeting the historical characteristics of fraud

Use Case – 3: Clustering and Association

  • Group transaction by providers or trnasaction types
  • Find group events by using the association algorithms
  • Perform outlier identification analysis for further scrutiny

Benefits of Fraud Detecting using CRISP DM

  • Lends itself as a systematic tool and methodology set to detect and prevent fraud
  • Helps to maximize the investigative efforts in auditing fraudulent transactions
  • Results in higher recoupments
  • Continually updates the model to identify new emerging abuse patterns






Publish Date: February 17, 2016 2:08 PM





About us - in 60 seconds!

Submit Event

Upcoming Events

The 19th Asia Pacific Annual Best Practices Conferences are here! Meeting Point for the World's Best Contact Center & CX Companies Read More...
The 19th World Final Annual Best Practices Conferences are here! Meeting Point for the World's Best Contact Center & CX Companies Read More...

Newsletter Registration

Please check to agree to be placed on the eNewsletter mailing list.

Industry Champion Award Leaderboard

Most active award entrants in the past 48 hours! - Vote for Others / About Program
both ids empty
session userid =
session UserTempID =
session adminlevel =
session blnTempHelpChatShow =
session cookie set = True
session page-view-total = 1
session page-view-total = 1
applicaiton blnAwardsClosed =
session blnCompletedAwardInterestPopup =
session blnCheckNewsletterInterestPopup =
session blnCompletedNewsletterInterestPopup =