Infigos Software Solutions Ltd

There are various kinds of frauds such as credit card fraud, insurance fraud, health care fraud, money laundry, identity fraud, and tax fraud. In credit card fraud, there are application fraud and behavioural fraud in credit card.
Learn more at: https://www.infigos-solutions.com/introduction-to-frauds/



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232

Fraud detection refers to applying the fraud model on new observations. Fraud investigation is carried out on the cases flagged fraud by the fraud detection system. Once fraud is confirmed then we check the accuracy of fraud model used.

Read more at: https://www.infigos-solutions.com/challenges-of-building-fraud-detection-and-prevention-systems/



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232

Expert driven fraud detection and prevention technique represents a good starting point and complementary to other approach used. Alternatively, used techniques should be accurate, operationally efficient and cost efficient. All these qualities can be observed in statistically used credit score card or customer churn model.

Read more at: https://www.infigos-solutions.com/challenges-of-building-fraud-detection-and-prevention-systems/



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232

Fraud Definition: An organization loses typical 5% of revenue due to fraud each year. As per Oxford dictionary, fraud is defined as wrongful or criminal deception intended to result in financial or personal gain. The most common characteristics of frauds are being uncommon, impeccably concealed, time evolving and carefully organized.

Read More at: https://www.infigos-solutions.com/introduction-to-frauds/



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232

Fraud Analyser supports remote team coordination with a feature set that is strong on visual characteristics such as,
1) Drop-down functionality to select different modelling techniques
2) Data manipulation functionality to remove or encode specific variables
3) Dashboards to track progress of data scientist activity and model development
4) Model comparator views
5) Periodic customer reports.But that’s not all.
To differentiate the solution from industry standards such as IBM ODM and FICO Blaze Advisor, Fraud Analyser comes with a dedicated data support team, so that product adoption is swift and easy.The platform is not sticky and enables easy exporting of work when done periodically.

Explore Usecases: https://www.infigos-solutions.com/case-studies/



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232

This case study is related with auto finance fraud. Here we managed to cut down the losses of due to repossession and fraud by $3mm on 50% test population within 6 months after machine learning model implementation.

Read More at: https://www.infigos-solutions.com/case-studies/



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232

This case study is related with fraud is credit cards. While onboarding Customers, Bank Identified Customers as Prime (with good credit standing) and Sub-Prime (with poor Credit History or Bureau Score), these Sub-Prime Customers are mostly rejected by other lenders and this is sizeable market and opportunity for the Bank to offer Credit Card (with lower Credit Limit and harsher punishment for default) and as such it is expected that Default/Losses and Frauds would be higher from this Segment. Prior to Mid Stage Risk Based Model Development the existing model was based on Origination Characteristics (When Customer Applied for Credit Card) and Bank continued to maintain separate Collections/Recovery Agencies (both Internal and Off-Shore Outsourced) for Prime and Sub-Prime Customers respectively.

Read More at: https://www.infigos-solutions.com/case-studies/



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232

Due to ineffective Fraud Detection and Prevention Techniques, the average TAT for claim processing is significantly on the rise. Further to make matters worse, there is steady increase in claims filing, and this claim set contains a significant Fraud Subset. Thus, the necessity of investing in effective and efficient model building and continuously improving to ensure relevance.

Read More at: https://www.infigos-solutions.com/case-studies/



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232

This case study is related with fraud is health insurance claims. A Claims Constraint Scoring Mechanism is required to be implemented which would Identify (with a significant level of accuracy) the Fraudulent Claims from the Genuine Claims. This Scoring methodology developed from historical Claims information would be effective in Rejecting Fraudulent Claims. A model was built using historical information.
Read More at: https://www.infigos-solutions.com/case-studies/



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232

This case study are related with fraud is insurance claims. A Claims Constraint Scoring Mechanism is required to be implemented which would Identify (with a significant level of accuracy) the Fraudulent Claims from the Genuine Claims.

Read More at: https://www.infigos-solutions.com/case-studies/



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232

Fraud Definition: An organization loses typical 5% of revenue due to fraud each year. As per Oxford dictionary, fraud is defined as wrongful or criminal deception intended to result in financial or personal gain. The most common characteristics of frauds are being uncommon, impeccably concealed, time evolving and carefully organized. Fraud cases are covered by lots of non-fraudulent cases, only minority are fraud cases; hence fraud is uncommon. It makes harder to detect frauds. Fraudster try to blend in and not behave different from normal behaviour, it makes fraud impeccably concealed. Fraudsters change their way of doing fraud with time in order not to get caught, hence its time evolving. Fraudsters often operate in group to avoid getting caught, and hence they plan carefully, its organized crime.

Read More at: https://www.infigos-solutions.com/introduction-to-frauds/



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232

The machine learning layer connects the rule-detection and relevant modelling functions. It starts by generating the rules-based framework from the initial training dataset by using most relevant machine learning algorithms.

Read More at: https://www.infigos-solutions.com/fraud-analyser/



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232

Fraud in finance is defined as claiming money from banks or insurance company by deceiving them by wrong means. There are predominately following kinds of fraud, which is costing finance industry over 300 Billion Pounds worldwide.

CREDIT CARD FRAUD:
In the case of credit card fraud, money is claimed from acquiring or merchant bank by customers by fraudulent means. In normal process, customer buys a product from merchant.

INSURANCE FRAUD:
In insurance sector, there are predominately two kinds of frauds – application fraud and claim fraud.

Know more at: https://www.infigos-solutions.com/fraud-analyser/



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232

Fraud Analyser is a SaaS product with an in-house data science support team bundled in. Priced on an annual subscription basis, it delivers a highly customisable solution that is user-friendly, effective and competitively priced.

Sign Up for Demo: https://knitin7005.typeform.com/to/uT4L5I



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232

Expert driven fraud detection and prevention technique represents a good starting point and complementary to other approach used. Alternatively, used techniques should be accurate, operationally efficient and cost efficient.

Know more at: https://www.infigos-solutions.com/challenges-of-building-fraud-detection-and-prevention-systems/



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232

Predominately, there are two kinds of solutions in the market for fraud detection and prevention,
1) Based on Rule Engine
2) Based on AI.
Read more at: https://www.infigos-solutions.com/rule-engine-implementation/



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232

Once a score card is built it can be used for prediction of fraud and non-fraud cases. If we have used the techniques such as logistic regression for building the final scorecard then it makes sense to set a cut-off of fraud score to differentiate between fraud and non-fraud cases.
Read more at: https://www.infigos-solutions.com/score-card-development/



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232

Fraudster achieve personal gains, which comes at the cost of other honest member of society. To compensate again the loss due to fraud, insurance company will increase the cost of premium.

Know more at: https://www.infigos-solutions.com/introduction-to-frauds/



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232

Fraud detection refers to applying the fraud model on new observations. Fraud investigation is carried out on the cases flagged fraud by the fraud detection system. Once fraud is confirmed then we check the accuracy of fraud model used.

Konw more at: https://www.infigos-solutions.com/challenges-of-building-fraud-detection-and-prevention-systems/



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232

Motivation behind the fraud: Motivation behind fraud can be explained by fraud triangle. Three possible reason for a someone to do frauds are,
Pressure: it is first component of fraud triangle, there could be financial and work pressure in the current situation. It’s not possible to resolve it by authorized means.

Opportunity: its second component of fraud triangle and fraudster see the fraud opportunity to deal with current situation. Internal controls are weak.

Rationalization: its psychological mechanism by which fraudsters try to justify the fraud, they commit. They think that its acceptable behaviour in current situation.

Read more at: https://www.infigos-solutions.com/introduction-to-frauds/



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232

Fraud analyser implements both white box and black box supervised techniques to build the model, depending upon requirement appropriate technique is used.

Read more at: https://www.infigos-solutions.com/challenges-of-building-fraud-detection-and-prevention-systems/



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232

Fraud detection refers the ability to detect the various kinds of frauds, while prevention refers the measure, which we can take in order to stop the fraud happening. Both techniques complement each other.

Read more at: https://www.infigos-solutions.com/introduction-to-frauds/



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232

Dataset variable are identified based on business problem. Those tables with such variables are identified and merged to create the final dataset. This data is cleaned to ensure the consistency of format across different variables.

Read more at: https://www.infigos-solutions.com/challenges-of-building-fraud-detection-and-prevention-systems/



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232

First this dataset is analysed for missing values if missing values exist then missing value indicator is inserted. Now this dataset is divided in to two stratified sample in ratio of 2:1 train and valid datasets respectively.

Read more at: https://www.infigos-solutions.com/score-card-development/



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232

To keep the things simple, it’s good to involve the people to manage the process. Process is managed by team based in Europe and India. There are two kinds of processes
1. Rule generation process managed by data scientist
2. Rule management process managed by rule engine expert
Read More at: https://www.infigos-solutions.com/people-and-process-management/



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232

1) Based on Rule Engine
2) Based on AI
It’s not possible to support the instant decision making by using the AI engine, since it can’t handle the real time data directly. For instant decision making, rule engines are used universally.
Read More at: https://www.infigos-solutions.com/rule-engine-implementation/



For more details,
Website: https://www.infigos-solutions.com/
Email: [email protected]
Phone: +353 1254 8232