Project Objectives Provider Fraud is one of the biggest problems facing Medicare. According to the government, the total Medicare spending increased exponentially due to frauds in Medicare claims. Healthcare fraud is an organized crime which involves peers of providers, physicians, beneficiaries acting together to make fraud claims.

Rigorous analysis of Medicare data has yielded many physicians who indulge in fraud. They adopt ways in which an ambiguous diagnosis code is used to adopt costliest procedures and drugs. Insurance companies are the most vulnerable institutions impacted due to these bad practices. Due to this reason, insurance companies increased their insurance premiums and as result healthcare is becoming costly matter day by day.

Healthcare fraud and abuse take many forms. Some of the most common types of frauds by providers are:

a) Billing for services that were not provided.

b) Duplicate submission of a claim for the same service.

c) Misrepresenting the service provided.

d) Charging for a more complex or expensive service than was actually provided.

e) Billing for a covered service when the service actually provided was not covered.

Problem Statement The goal of this project is to " predict the potentially fraudulent providers " based on the claims filed by them.along with this, we will also discover important variables helpful in detecting the behaviour of potentially fraud providers. further, we will study fraudulent patterns in the provider's claims to understand the future behaviour of providers.

Introduction to the Dataset For the purpose of this project, we are considering Inpatient claims, Outpatient claims and Beneficiary details of each provider. Lets s see their details :

A) Inpatient Data

This data provides insights about the claims filed for those patients who are admitted in the hospitals. It also provides additional details like their admission and discharge dates and admit d diagnosis code.

B) Outpatient Data

This data provides details about the claims filed for those patients who visit hospitals and not admitted in it.

C) Beneficiary Details Data

This data contains beneficiary KYC details like health conditions,regioregion they belong to etc.

Context

There is a lack of public available datasets on financial services and specially in the emerging mobile money transactions domain. Financial datasets are important to many researchers and in particular to us performing research in the domain of fraud detection. Part of the problem is the intrinsically private nature of financial transactions, that leads to no publicly available datasets.

We present a synthetic dataset generated using the simulator called PaySim as an approach to such a problem. PaySim uses aggregated data from the private dataset to generate a synthetic dataset that resembles the normal operation of transactions and injects malicious behaviour to later evaluate the performance of fraud detection methods.

Content

PaySim simulates mobile money transactions based on a sample of real transactions extracted from one month of financial logs from a mobile money service implemented in an African country. The original logs were provided by a multinational company, who is the provider of the mobile financial service which is currently running in more than 14 countries all around the world.

This synthetic dataset is scaled down 1/4 of the original dataset and it is created just for Kaggle.

NOTE: Transactions which are detected as fraud are cancelled, so for fraud detection these columns (oldbalanceOrg, newbalanceOrig, oldbalanceDest, newbalanceDest ) must not be used.

Headers

This is a sample of 1 row with headers explanation:

1,PAYMENT,1060.31,C429214117,1089.0,28.69,M1591654462,0.0,0.0,0,0

step - maps a unit of time in the real world. In this case 1 step is 1 hour of time. Total steps 744 (30 days simulation).

type - CASH-IN, CASH-OUT, DEBIT, PAYMENT and TRANSFER.

amount - amount of the transaction in local currency.

nameOrig - customer who started the transaction

oldbalanceOrg - initial balance before the transaction

newbalanceOrig - new balance after the transaction.

nameDest - customer who is the recipient of the transaction

oldbalanceDest - initial balance recipient before the transaction. Note that there is not information for customers that start with M (Merchants).

newbalanceDest - new balance recipient after the transaction. Note that there is not information for customers that start with M (Merchants).

isFraud - This is the transactions made by the fraudulent agents inside the simulation. In this specific dataset the fraudulent behavior of the agents aims to profit by taking control or customers accounts and try to empty the funds by transferring to another account and then cashing out of the system.

isFlaggedFraud - The business model aims to control massive transfers from one account to another and flags illegal attempts. An illegal attempt in this dataset is an attempt to transfer more than 200.000 in a single transaction.

Past Research

There are 5 similar files that contain the run of 5 different scenarios. These files are better explained at my PhD thesis chapter 7 (PhD Thesis Available here http://urn.kb.se/resolve?urn=urn:nbn:se:bth-12932.

We ran PaySim several times using random seeds for 744 steps, representing each hour of one month of real time, which matches the original logs. Each run took around 45 minutes on an i7 intel processor with 16GB of RAM. The final result of a run contains approximately 24 million of financial records divided into the 5 types of categories: CASH-IN, CASH-OUT, DEBIT, PAYMENT and TRANSFER.

Acknowledgements

This work is part of the research project ”Scalable resource-efficient systems for big data analytics” funded by the Knowledge Foundation (grant: 20140032) in Sweden.

Please refer to this dataset using the following citations:

PaySim first paper of the simulator:

E. A. Lopez-Rojas , A. Elmir, and S. Axelsson. "PaySim: A financial mobile money simulator for fraud detection". In: The 28th European Modeling and Simulation Symposium-EMSS, Larnaca, Cyprus. 2016

Credit Card Fraud Detection Analysis and Preprocessing -

Introduction, Data Source, and Project Goal This project presents an Exploratory Data Analysis (EDA) and strategic data preparation for a credit card fraud detection dataset. The dataset, sourced from Kaggle, contains over 280,000 records. The primary challenge identified is extreme class imbalance, as less than 0.2% of transactions are fraudulent. The goal is to prepare the data for a classification model capable of predicting whether… See the full description on the dataset page: https://huggingface.co/datasets/Barvero/Credit_Card_Fraud_Analysis_Project.

This dataset is a valuable resource for building and evaluating machine learning models to predict fraudulent transactions in an e-commerce environment. With 6.3 million rows, it provides a rich, real-world scenario for data science tasks.

The data is an excellent case study for several key challenges in machine learning, including:

• Handling Imbalanced Data: The dataset is highly imbalanced, as legitimate transactions vastly outnumber fraudulent ones. This necessitates the use of specialized techniques like SMOTE or advanced models like XGBoost that can handle class imbalance effectively.

• Feature Engineering: The raw data provides an opportunity to create new, more powerful features, such as transaction velocity or the ratio of account balances, which can improve model performance.

• Model Evaluation: Traditional metrics like accuracy are misleading for this type of dataset. The project requires a deeper analysis using metrics such as Precision, Recall, F1-Score, and the Precision-Recall AUC to truly understand the model's effectiveness.

Key Features: The dataset includes a variety of anonymized transaction details:

• amount: The value of the transaction.

• type: The type of transaction (e.g., TRANSFER, CASH_OUT).

• oldbalance & newbalance: The balances of the origin and destination accounts before and after the transaction.

• isFraud: The target variable, a binary flag indicating a fraudulent transaction.

The global healthcare fraud detection and investigation software market is experiencing robust growth, driven by increasing healthcare expenditures, rising instances of fraudulent activities, and the escalating need for robust security measures within the healthcare sector. The market's expansion is fueled by technological advancements, including the adoption of artificial intelligence (AI), machine learning (ML), and big data analytics to identify complex fraud patterns and anomalies. These technologies allow for more proactive fraud detection, reducing financial losses and improving healthcare system efficiency. Furthermore, stringent government regulations and increased penalties for healthcare fraud are pushing healthcare providers and payers to adopt advanced software solutions. This market segment is witnessing a shift towards cloud-based solutions offering scalability, cost-effectiveness, and accessibility. However, challenges such as data privacy concerns, integration complexities with existing systems, and the high cost of implementing and maintaining these sophisticated systems act as restraints to market growth. Considering a conservative CAGR of 15% (a common rate for rapidly developing software markets) from a base year of 2025 with a market size of $2 billion, and a forecast period of 2025-2033, we can project significant expansion. The competitive landscape is dynamic, with established players like SAS and Fujitsu alongside specialized firms like DataWalk and WhiteHat AI competing for market share. These companies offer diverse solutions, catering to various healthcare stakeholders' needs. The ongoing innovation in fraud detection techniques and the increasing focus on interoperability among different healthcare systems will further shape the market's trajectory. The market is segmented by software type (e.g., rule-based systems, AI-powered systems), deployment mode (cloud, on-premise), and end-user (hospitals, insurance companies, government agencies). Geographic segmentation would likely show strong growth in North America and Europe initially, followed by expansion in other regions as awareness and adoption increase. The increasing sophistication of fraud schemes necessitates continuous innovation in software capabilities, ensuring the market's long-term growth potential.

Problem Statement:

Company ABC, a major credit card company, faces challenges with their existing fraud detection system. The current system exhibits slow responsiveness in recognizing new patterns of fraud, leading to significant financial losses. To address this issue, they have contracted us to design and implement an algorithm that can efficiently identify and flag potentially fraudulent transactions for further investigation. The data provided consists of two tables: "cc_info," containing general credit card and cardholder information, and "transactions," containing details of credit card transactions that occurred between August 1st and October 30th.

Objective: The primary goal of this project is to build an advanced fraud detection system using neural networks to identify transactions that appear unusual and potentially fraudulent. By applying object-oriented programming (OOPs) concepts, we aim to develop a scalable and modular solution that can handle large volumes of data and provide valuable insights to Company ABC.

Data Dictionary

We have two files in our dataset cc_info.csv and transactions.csv

Here is the column description for cc_info.csv

Here is the column description for transactions.csv

Community Rebuild Permit Fraud Detection Market Outlook

According to our latest research, the global Community Rebuild Permit Fraud Detection market size reached $1.16 billion in 2024. The market is experiencing robust expansion, driven by the increasing adoption of digital permitting systems and heightened regulatory scrutiny. With a projected compound annual growth rate (CAGR) of 14.2% from 2025 to 2033, the market is forecasted to attain a value of $3.34 billion by 2033. This growth is primarily fueled by the rising incidences of permit fraud in community rebuild initiatives, advancements in fraud detection technologies, and a global push towards transparent and accountable urban development processes.

One of the primary growth factors in the Community Rebuild Permit Fraud Detection market is the rapid digitalization of permitting processes across municipalities and regulatory agencies. As more cities and local governments migrate to digital platforms for managing construction and rebuild permits, the risk and complexity of fraudulent activities have increased. This shift has necessitated the deployment of advanced fraud detection solutions capable of analyzing large volumes of data, identifying anomalies, and providing real-time alerts. The integration of artificial intelligence (AI), machine learning, and data analytics into these solutions has significantly enhanced their effectiveness, enabling organizations to proactively detect and prevent fraudulent permit applications. Additionally, the growing awareness among stakeholders about the financial and reputational risks associated with permit fraud is driving investments in robust detection systems.

Another significant driver for the market is the tightening of regulatory frameworks and compliance requirements at both local and national levels. Governments worldwide are implementing stricter regulations to curb fraudulent activities in construction and community rebuild projects, particularly in the aftermath of natural disasters or large-scale urban redevelopment efforts. These regulations often mandate the use of secure, auditable, and transparent permitting systems, which in turn increases the demand for specialized fraud detection software and services. Furthermore, public pressure for accountability and transparency in government spending has made it imperative for municipalities and regulatory agencies to adopt state-of-the-art fraud detection mechanisms. This regulatory landscape is fostering innovation and encouraging solution providers to develop more sophisticated and customizable tools tailored to the unique needs of different jurisdictions.

The increasing frequency and scale of community rebuild projects, especially in disaster-prone regions, also contribute to the market’s growth. As urban areas expand and infrastructure ages, the volume of permit applications rises, creating more opportunities for fraudulent actors to exploit system vulnerabilities. In response, construction companies, municipalities, and regulatory bodies are prioritizing investments in fraud detection solutions to safeguard public funds, maintain project timelines, and uphold community trust. The collaboration between public and private sector entities, along with the emergence of public-private partnerships, is further accelerating the adoption of advanced fraud detection technologies. This trend is expected to continue as stakeholders recognize the long-term value of proactive fraud mitigation in ensuring the success and sustainability of community rebuild initiatives.

From a regional perspective, North America currently dominates the Community Rebuild Permit Fraud Detection market, accounting for the largest share in 2024. This leadership position is attributed to the region’s advanced digital infrastructure, high incidence of construction and rebuild projects, and stringent regulatory environment. Europe follows closely, driven by the European Union’s emphasis on transparency and anti-fraud measures in public sector projects. The Asia Pacific region is witnessing the fastest growth, fueled by rapid urbanization, increasing government investments in smart city initiatives, and a growing awareness of the need for fraud prevention in the construction sector. Meanwhile, Latin America and the Middle East & Africa are gradually adopting fraud detection solutions as part of broader efforts to modernize their permitting processes and improve governance standards.

Community Rebuild Permit Fraud Detection Market Outlook

According to our latest research, the global Community Rebuild Permit Fraud Detection market size reached USD 2.18 billion in 2024, with robust momentum driven by increasing incidences of fraudulent activities in construction permitting processes worldwide. The market is expected to expand at a CAGR of 12.4% from 2025 to 2033, resulting in a forecasted market size of USD 6.18 billion by 2033. This remarkable growth is primarily fueled by the rising adoption of advanced analytics, artificial intelligence, and machine learning technologies to counteract sophisticated fraud schemes targeting community rebuild permits.

The surge in fraudulent activities within the construction and rebuilding sector has become a substantial concern for municipalities, insurance providers, and construction companies alike. Fraudulent permit applications, identity theft, and document forgery have led to significant financial losses and undermined public trust in governmental systems. As a result, there is a growing emphasis on deploying robust fraud detection solutions that can automate the verification process, flag suspicious activities, and ensure compliance with regulatory standards. The integration of predictive analytics and real-time monitoring tools has empowered stakeholders to proactively identify and mitigate risks, thereby safeguarding community assets and ensuring the integrity of rebuilding initiatives.

Technological advancements have played a pivotal role in shaping the community rebuild permit fraud detection market. The proliferation of cloud-based platforms and the incorporation of artificial intelligence have enabled real-time data sharing and analysis, enhancing the accuracy and efficiency of fraud detection mechanisms. Furthermore, the advent of blockchain technology has introduced an additional layer of transparency and immutability to permit records, making it increasingly difficult for malicious actors to manipulate or forge documentation. These innovations have not only improved detection rates but have also streamlined permit processing workflows, reducing administrative burdens and operational costs for end-users.

Another significant growth driver is the tightening of regulatory frameworks governing construction permits and insurance claims. Governments across the globe are enacting stricter compliance mandates and digital transformation initiatives, compelling municipalities and construction firms to invest in advanced fraud detection systems. Heightened awareness of the repercussions of permit fraud, coupled with the increasing digitization of public services, has accelerated market adoption rates. Additionally, the growing prevalence of public-private partnerships in urban development projects has further underscored the necessity for robust fraud prevention measures, fostering a culture of accountability and transparency within the sector.

From a regional perspective, North America currently leads the community rebuild permit fraud detection market, accounting for the largest share owing to its well-established regulatory environment and high incidence of construction-related fraud. Europe follows closely, driven by stringent compliance requirements and widespread adoption of digital permit management systems. The Asia Pacific region is anticipated to witness the fastest growth over the forecast period, supported by rapid urbanization, infrastructure investments, and increasing government focus on combating fraudulent activities in the construction sector. Meanwhile, Latin America and the Middle East & Africa are gradually embracing advanced fraud detection solutions, albeit at a slower pace due to infrastructural and economic challenges.

Component Analysis

The community rebuild permit fraud detection market is segmented by component into software and services, each playing a vital role in the holistic implementation of anti-fraud strategies. The software segment dominates the market, accounting for over 65% of the total revenue in 2024. This dominance is attributed to the increasing deployment of advanced analytics platforms, machine learning algorithms, and blockchain-based solutions that automate the detection of fraudulent permit applications and ensure data integrity. These software solutions offer real-time monitoring, predictive modeling, and customizable rule engines, enabling municipalities and

Description

This synthetic dataset, "Fraudulent E-Commerce Transactions," is designed to simulate transaction data from an e-commerce platform with a focus on fraud detection. It contains a variety of features commonly found in transactional data, with additional attributes specifically engineered to support the development and testing of fraud detection algorithms.

Dataset Overview

• Number of Transactions in Version 1: 1,472,952
• Number of Transactions in Version 2: 23,634
• Features: 16
• Fraudulent Transactions: Approximately 5%

Feature Details

1. Transaction ID: A unique identifier for each transaction.
2. Customer ID: A unique identifier for each customer.
3. Transaction Amount: The total amount of money exchanged in the transaction.
4. Transaction Date: The date and time when the transaction took place.
5. Payment Method: The method used to complete the transaction (e.g., credit card, PayPal, etc.).
6. Product Category: The category of the product involved in the transaction.
7. Quantity: The number of products involved in the transaction.
8. Customer Age: The age of the customer making the transaction.
9. Customer Location: The geographical location of the customer.
10. Device Used: The type of device used to make the transaction (e.g., mobile, desktop).
11. IP Address: The IP address of the device used for the transaction.
12. Shipping Address: The address where the product was shipped.
13. Billing Address: The address associated with the payment method.
14. Is Fraudulent: A binary indicator of whether the transaction is fraudulent (1 for fraudulent, 0 for legitimate).
15. Account Age Days: The age of the customer's account in days at the time of the transaction.
16. Transaction Hour: The hour of the day when the transaction occurred.

Purpose

The dataset is intended for use in developing and testing machine learning models for fraud detection in e-commerce transactions. It can also be used for exploratory data analysis, feature engineering, and benchmarking fraud detection algorithms.

Generation Method

The data is entirely synthetic, generated using Python's Faker library and custom logic to simulate realistic transaction patterns and fraudulent scenarios. The dataset is not based on real individuals or transactions and is created for educational and research purposes.

Usage

Feel free to use this dataset for data analysis, machine learning projects, or as a benchmark for fraud detection algorithms. If you use this dataset in your research or projects, please provide proper attribution.

Dataset Overview

Introduction

This dataset presents a synthetic representation of mobile money transactions, meticulously crafted to mirror the complexities of real-world financial activities while integrating fraudulent behaviors for research purposes. Derived from a simulator named PaySim, which utilizes aggregated data from actual financial logs of a mobile money service in an African country, this dataset aims to fill the gap in publicly available financial datasets for fraud detection studies. It encompasses a variety of transaction types including CASH-IN, CASH-OUT, DEBIT, PAYMENT, and TRANSFER over a simulated period of 30 days, providing a comprehensive environment for evaluating fraud detection methodologies. By addressing the intrinsic privacy concerns associated with financial transactions, this dataset offers a unique resource for researchers and analysts in the field of financial security and fraud detection, scaled to 1/4 of the original dataset size for efficient use within the Kaggle platform. Please note that transactions marked as fraudulent have been nullified, emphasizing the importance of non-balance columns for fraud analysis. This dataset is a contribution to the field from the "Scalable resource-efficient systems for big data analytics" project, funded by the Knowledge Foundation in Sweden.

Dataset Details

PaySim synthesizes mobile money transactions using data derived from a month's worth of financial logs from a mobile money service operating in an African country. These logs were provided by a multinational company that offers this financial service across more than 14 countries globally.

This synthetic dataset has been scaled to one-quarter the size of the original dataset and is specifically tailored for Kaggle.

Important Note: Transactions identified as fraudulent are annulled. Hence, for fraud detection analysis, the following columns should not be utilized: oldbalanceOrg, newbalanceOrig, oldbalanceDest, newbalanceDest.

Dataset Structure

• step: Represents a unit of time in the real world, with 1 step equating to 1 hour. The total simulation spans 744 steps, equivalent to 30 days.
• type: Transaction types include CASH-IN, CASH-OUT, DEBIT, PAYMENT, and TRANSFER.
• amount: The transaction amount in the local currency.
• nameOrig: The customer initiating the transaction.
• oldbalanceOrg: The initial balance before the transaction.
• newbalanceOrig: The new balance after the transaction.
• nameDest: The transaction's recipient customer.
• oldbalanceDest: The initial recipient's balance before the transaction. Not applicable for customers identified by 'M' (Merchants).
• newbalanceDest: The new recipient's balance after the transaction. Not applicable for 'M' (Merchants).
• isFraud: Identifies transactions conducted by fraudulent agents aiming to deplete customer accounts through transfers and cash-outs.
• isFlaggedFraud: Flags large-scale, unauthorized transfers between accounts, with any single transaction exceeding 200,000 being considered illegal.

Previous Research and Acknowledgments

This dataset has been generated through multiple runs of the PaySim simulator, each simulating a month of real-time transactions over 744 steps. Each run produced approximately 24 million financial records across the five transaction categories.

This project is part of the "Scalable resource-efficient systems for big data analytics" research, supported by the Knowledge Foundation (grant: 20140032) in Sweden.

For citations and further references, please use:

E. A. Lopez-Rojas, A. Elmir, and S. Axelsson. "PaySim: A financial mobile money simulator for fraud detection". In: The 28th European Modeling and Simulation Symposium-EMSS, Larnaca, Cyprus. 2016

This work has received funding from the European Union's Horizon 2020 research and innovation framework programme under grant agreement No. 644869 (DICE), the Spanish Government (Ministerio de Economía y Competitividad) under project No. TIN2013- 46238-C4-1-R and The Aragonese Goverment Ref. T27 – DIStributed COmputation (DISCO) research group.

It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.

About Data

The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.

It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.

Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification.

Acknowledgements

The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project

Please cite the following works:

Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015

Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon

Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE

Dal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi)

Carcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-Aël; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier

Carcillo, Fabrizio; Le Borgne, Yann-Aël; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing

Bertrand Lebichot, Yann-Aël Le Borgne, Liyun He, Frederic Oblé, Gianluca Bontempi Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection, INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019

Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Frederic Oblé, Gianluca Bontempi Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection Information Sciences, 2019

Yann-Aël Le Borgne, Gianluca Bontempi Reproducible machine Learning for Credit Card Fraud Detection - Practical Handbook

Bertrand Lebichot, Gianmarco Paldino, Wissam Siblini, Liyun He, Frederic Oblé, Gianluca Bontempi Incremental learning strategies for credit cards fraud detection, IInternational Journal of Data Science and Analytics

Claims Fraud Detection via Telematics Market Outlook

According to our latest research, the Global Claims Fraud Detection via Telematics market size was valued at $1.8 billion in 2024 and is projected to reach $6.5 billion by 2033, expanding at a CAGR of 15.2% during the forecast period of 2025–2033. A primary factor driving this robust growth is the accelerating adoption of telematics technologies by insurance companies seeking to mitigate losses from fraudulent claims, especially within the automobile insurance segment. As digital transformation and connected devices become embedded in the insurance sector, telematics-based solutions are increasingly leveraged to provide real-time data, enabling more accurate claims assessments and reducing the incidence of fraud. This convergence of advanced analytics, machine learning, and IoT-driven data is fundamentally reshaping how insurers identify, investigate, and prevent fraudulent activities, thereby enhancing operational efficiency and customer trust on a global scale.

Regional Outlook

North America currently holds the largest share of the global Claims Fraud Detection via Telematics market, accounting for approximately 39% of the total market value in 2024. This dominance is attributed to the region’s mature insurance sector, widespread adoption of telematics devices, and the presence of several leading technology providers specializing in fraud detection. Regulatory mandates on data transparency and consumer protection in the United States and Canada have further propelled the integration of telematics into insurance operations, particularly within automobile and property insurance domains. Additionally, insurers in North America are early adopters of AI and machine learning algorithms for predictive analytics, making the region a hotbed for innovation and pilot projects in claims fraud detection. The region’s robust IT infrastructure and substantial investments in R&D have also accelerated the deployment of cloud-based fraud detection solutions, allowing insurers to scale their operations and enhance claims accuracy.

Asia Pacific is emerging as the fastest-growing region in the Claims Fraud Detection via Telematics market, with a projected CAGR of 18.7% from 2025 to 2033. This rapid expansion is driven by the increasing penetration of connected vehicles, the proliferation of digital insurance platforms, and rising awareness about insurance fraud, particularly in China, India, and Japan. Governments across the region are actively promoting digital transformation in the insurance sector, with several policy initiatives aimed at encouraging telematics adoption for both commercial and personal lines. The influx of foreign direct investment (FDI) and the establishment of regional innovation hubs have further stimulated the development and deployment of advanced telematics solutions. Startups and established players alike are collaborating with telecom operators and automotive OEMs to embed telematics into vehicles, providing insurers with granular data for more effective fraud detection and risk assessment.

In emerging economies across Latin America and the Middle East & Africa, the adoption of claims fraud detection via telematics is still in its nascent stages but is gaining momentum due to increasing insurance penetration and government-led digitalization drives. However, these markets face unique challenges, including limited infrastructure, lower consumer awareness, and regulatory uncertainties that can hamper telematics adoption. Local insurers are gradually realizing the potential of telematics to curb rising insurance fraud, especially in automobile and property insurance segments. Pilot programs and partnerships with global technology providers are helping to bridge the knowledge and technology gaps. As regulatory frameworks evolve and the cost of telematics hardware decreases, these regions are expected to witness accelerated adoption, albeit at a slower pace compared to North America and Asia Pacific.

Report Scope

The global Synthetic Data Solution market is experiencing robust growth, projected to reach an estimated market size of approximately $1,500 million by 2025, with a Compound Annual Growth Rate (CAGR) of around 25% from 2019 to 2033. This significant expansion is primarily propelled by the increasing demand for privacy-preserving data generation, especially within sensitive sectors like financial services and healthcare, where regulations around data privacy are stringent. The retail industry is also a key driver, leveraging synthetic data for enhanced customer analytics, personalized marketing, and fraud detection without compromising consumer privacy. Furthermore, the burgeoning adoption of AI and machine learning across various industries necessitates vast amounts of high-quality training data, a need that synthetic data effectively addresses by overcoming limitations of real-world data scarcity and bias. The shift towards cloud-based solutions is also accelerating market penetration, offering scalability, flexibility, and cost-effectiveness for businesses of all sizes. Despite the promising growth trajectory, the market faces certain restraints. The complexity and cost associated with developing sophisticated synthetic data generation models, alongside concerns regarding the potential for bias inherited from the underlying real data, pose challenges. Ensuring the statistical fidelity and representativeness of synthetic data to real-world scenarios remains a critical area of focus for solution providers. However, ongoing advancements in generative adversarial networks (GANs) and other AI techniques are continuously improving the quality and realism of synthetic data. Geographically, North America currently leads the market due to its early adoption of AI technologies and strong regulatory frameworks promoting data privacy. Asia Pacific is emerging as a high-growth region, fueled by rapid digital transformation and increasing investments in AI research and development by countries like China and India. The market is characterized by intense competition among established tech giants and innovative startups, driving continuous innovation in synthetic data generation methodologies and applications. This in-depth report offers a panoramic view of the global Synthetic Data Solution market, providing a meticulous analysis of its current landscape, historical trajectory, and future potential. With a study period spanning from 2019 to 2033, and a base year of 2025, the report leverages comprehensive data from the historical period (2019-2024) to project a robust growth trajectory through the forecast period (2025-2033). The estimated market size for 2025 is projected to be in the hundreds of millions of US dollars, with significant expansion anticipated in the coming years.

The No-code AI Platform Market size was valued at USD 4.93 billion in 2023 and is projected to reach USD 31.95 billion by 2032, exhibiting a CAGR of 30.6 % during the forecasts period. No-code AI platform market refers to tools and applications that help the users create and implement AI without coding knowledge. Such platforms demystify the creation of AI solutions and effectively exclude the need for technical expertise which in turn speeds up the project. Some of the applications include, workflow automation, chatbot construction, and the creation of a predictive analytics model. This can be done in finance for purposes of fraud detection, in the retail line for customer analysis and in healthcare for observing patient’s health. Some of the trends are the usage of complex AI technologies like NLP and ML in No-Code environments, emergence of the Low-code/No-Code Hybrid models, and the shift towards the more accessible UI and more extensive adaptation for specific company requirements. Recent developments include: In October 2023, CyborgIntell, a prominent AI solutions provider, unveiled two new offerings tailored for the BFSI sector, Feature Store and Model Risk Management (MRM). Feature Store, a zero-code AI platform, automates the creation of thousands of new features from raw data, significantly reducing the time required for data preparation for modeling by 90%. This empowers financial institutions to analyze various aspects of their transactions, including behaviors, patterns, habits, preferences, risks, and relationships , In October 2023, Akkio Inc. introduced Generative Reports, an AI tool that instantly transforms data into actionable insights. This unique tool enables small and medium businesses to connect their data, describe projects, and automatically generate real-time reports. It offers a self-service solution for optimizing marketing spend, lead scoring, revenue forecasting, and enhancing customer experiences , In May 2023, Microsoft made an undisclosed investment in Builder.ai. This strategic collaboration was aimed at integrating Builder. Ai's AI assistant, Natasha, into Microsoft Teams video and chat software, enabling customers to create business apps seamlessly within the platform. Additionally, Builder.ai planned to enhance Natasha's capabilities by incorporating Microsoft's AI algorithms to achieve a more human-like conversational experience , In March 2023, Google LLC launched Gen App Builder, a new product designed to empower programmers in developing advanced generative AI applications, without machine learning proficiency. This product launch was aimed at enabling developers to seamlessly integrate experience into applications and websites into their applications and websites. With Google LLC's no-code conversational and search capabilities, this process is expected to take only a few minutes or hours .

Householding Analytics Market Outlook

According to our latest research, the global Householding Analytics market size reached USD 3.42 billion in 2024, reflecting robust adoption across multiple industries. The market is experiencing a healthy compound annual growth rate (CAGR) of 13.8% from 2025 to 2033. By the end of 2033, we project the Householding Analytics market to expand to USD 10.38 billion, driven by the increasing demand for advanced analytics in customer segmentation, marketing, and risk management. This growth is primarily fueled by the need for personalized customer engagement and the proliferation of big data analytics in enterprise decision-making.

The surge in demand for Householding Analytics is largely attributed to the increasing complexity of customer relationships and the growing necessity for businesses to understand household-level data. Enterprises, particularly in the BFSI and retail sectors, are leveraging these analytics to gain deeper insights into family structures, shared financial behaviors, and collective purchasing patterns. This enables organizations to tailor their products, services, and marketing strategies more effectively, thereby enhancing customer loyalty and lifetime value. The integration of artificial intelligence and machine learning algorithms with householding analytics platforms is further amplifying the accuracy and predictive capabilities of these solutions, making them indispensable for data-driven organizations.

Another key growth factor for the Householding Analytics market is the rising emphasis on fraud detection and risk assessment. Financial institutions and insurance companies are increasingly utilizing householding analytics to identify anomalous behavior patterns across related accounts, thereby mitigating the risk of fraud and improving regulatory compliance. The ability to consolidate individual data points into comprehensive household profiles allows these organizations to detect suspicious activity that might otherwise go unnoticed in isolated datasets. Additionally, regulatory requirements around data transparency and anti-money laundering are compelling organizations to adopt more sophisticated analytics tools, further accelerating market growth.

The rapid digital transformation across industries is also playing a pivotal role in propelling the adoption of Householding Analytics. As organizations transition to omnichannel engagement models, the volume of customer data generated across touchpoints has grown exponentially. Householding analytics platforms enable businesses to unify disparate data sources and extract actionable insights at the household level, facilitating targeted marketing campaigns, personalized product recommendations, and optimized resource allocation. The increasing availability of cloud-based analytics solutions is lowering the barriers to entry for small and medium enterprises (SMEs), democratizing access to advanced analytics and expanding the market’s addressable base.

From a regional perspective, North America currently dominates the Householding Analytics market, driven by the presence of leading analytics vendors and high digital maturity among enterprises. However, Asia Pacific is anticipated to witness the fastest growth over the forecast period, supported by rapid urbanization, expanding middle-class populations, and increasing investments in digital infrastructure. Europe continues to demonstrate steady growth, particularly in the BFSI and retail sectors, while Latin America and the Middle East & Africa are emerging as attractive markets due to ongoing digital transformation initiatives and rising awareness of advanced analytics solutions.

Component Analysis

The Householding Analytics market is segmented by component into Software and Services, each playing a crucial role in the overall ecosystem. Software solutions form the backbone of the market, providing the core functionalities required for data integration, analysis,

Claims Processing AI Market Outlook

According to our latest research, the global Claims Processing AI market size was valued at USD 2.1 billion in 2024 and is anticipated to reach USD 13.8 billion by 2033, expanding at a robust CAGR of 23.1% during the forecast period. This remarkable growth is primarily driven by the urgent need for automation, enhanced accuracy, and cost efficiency in insurance claims management, as insurers and related stakeholders increasingly adopt artificial intelligence to streamline operations and improve customer experience. The adoption of AI technologies is revolutionizing claims processing workflows by reducing manual intervention, accelerating settlement cycles, and minimizing fraudulent activities.

The rapid digital transformation in the insurance sector is a critical growth factor for the Claims Processing AI market. Insurers are under immense pressure to deliver seamless, error-free, and timely services to policyholders in an increasingly competitive landscape. AI-powered claims processing solutions are enabling insurance companies to automate data extraction, validate claims, and detect anomalies in real-time, significantly reducing the turnaround time and operational costs. The integration of machine learning, natural language processing, and advanced analytics is empowering insurers to handle large volumes of claims efficiently while maintaining compliance and quality standards. As a result, the demand for AI-driven claims solutions is surging among both established insurers and new-age insurtech firms.

Another major driver fueling market expansion is the growing incidence of insurance fraud and the escalating complexity of claims. Fraudulent claims cost the insurance industry billions of dollars annually, and traditional manual methods are often inadequate to detect sophisticated fraud patterns. AI algorithms can analyze massive datasets, identify suspicious behavior, and flag potentially fraudulent activities with high precision. This not only safeguards insurersÂ’ financial interests but also helps in building trust with genuine customers. Furthermore, the increasing adoption of digital channels for policy purchase and claims submission is generating vast amounts of unstructured data, which AI solutions can process and analyze efficiently, further boosting market growth.

The proliferation of cloud computing and scalable AI platforms is also accelerating the adoption of claims processing AI solutions. Cloud-based deployment models provide insurers with the flexibility to scale resources, reduce infrastructure costs, and ensure business continuity. The availability of AI-as-a-Service offerings allows even small and medium enterprises to leverage advanced claims processing capabilities without significant upfront investments. Additionally, regulatory mandates emphasizing transparency, data privacy, and customer-centricity are prompting insurers to modernize their claims management systems with AI, further spurring market expansion.

The integration of AI in claims processing is not limited to traditional insurance sectors. A burgeoning area of interest is the Construction Claims AI Analysis Service, which is gaining traction as construction projects become increasingly complex and data-driven. This service leverages AI to analyze construction claims, identify potential risks, and streamline the resolution process. By automating the analysis of vast amounts of project data, AI can detect patterns and anomalies that might indicate potential claims issues, thereby enabling construction firms to proactively address them. This not only helps in reducing the time and cost associated with claims resolution but also enhances the overall efficiency and transparency of construction project management.

Regionally, North America commands the largest share of the Claims Processing AI market, driven by the presence of leading insurance companies, advanced technological infrastructure, and supportive regulatory frameworks. Europe follows closely, with significant investments in digital transformation and AI adoption across the insurance sector. The Asia Pacific region is expected to witness the fastest CAGR, fueled by rapid urbanization, rising insurance penetration, and increasing focus on automation and fraud prevention. Latin America and the Middle East & Africa are also emerging as promising markets, as insurers in these

The global Insurance Digital Risk Control Service market is experiencing robust growth, projected to reach approximately $12,500 million by 2025, with an anticipated Compound Annual Growth Rate (CAGR) of 15% through 2033. This significant expansion is fueled by the increasing need for insurers to proactively manage and mitigate digital risks, driven by the escalating sophistication of cyber threats, the growing reliance on digital platforms for customer interaction and claims processing, and the evolving regulatory landscape demanding stronger data protection. The adoption of cloud-based solutions is a major trend, offering scalability, cost-effectiveness, and enhanced accessibility for risk control services. Furthermore, advancements in artificial intelligence and machine learning are enabling more sophisticated fraud detection, predictive analytics for risk assessment, and automated policy management, thereby streamlining operations and improving underwriting accuracy. The market's dynamism is also shaped by the diverse applications across key sectors. The financial services and automotive industries are leading the charge in adopting these services, recognizing their critical role in safeguarding sensitive data and ensuring operational continuity. The tourism industry is also increasingly leveraging these solutions to protect against online booking fraud and secure customer information. While the market benefits from these strong drivers and evolving technological integrations, potential restraints include the high initial investment costs associated with sophisticated digital risk control systems and a lingering shortage of skilled professionals to manage and implement these advanced solutions. Despite these challenges, the imperative for enhanced security and compliance in the digital age ensures continued market expansion and innovation. This report provides an in-depth analysis of the Insurance Digital Risk Control Service market, spanning from the historical period of 2019-2024 to a forecast period extending to 2033, with a base year of 2025. The study leverages extensive data and expert insights to project market dynamics, identify key growth drivers, and forecast future trends. The market is expected to witness significant expansion, driven by the increasing need for insurers to mitigate digital threats and enhance operational efficiency. We delve into the market's concentration, leading players, and the impact of technological advancements and regulatory landscapes. The projected market size for 2025 is estimated to be in the millions of dollars, with substantial growth anticipated over the forecast period.

Credit Card Fraud Detection Introduction Credit card fraud detection is a critical challenge in the financial sector. This project aims to build a machine learning model to identify fraudulent credit card transactions using a comprehensive dataset.

Dataset Overview The dataset contains transactions made by credit cards in September 2013 by European cardholders. It presents a significant class imbalance, with the majority of transactions being non-fraudulent.

Features:

Time: Seconds elapsed between this transaction and the first transaction in the dataset. V1 to V28: Anonymized features resulting from a PCA transformation. Amount: Transaction amount. Class: Target variable (1 for fraud, 0 for non-fraud). Steps Taken 1. Data Preprocessing Standardization: Standardized numeric features to improve model performance. Handling Imbalance: Applied SMOTE (Synthetic Minority Over-sampling Technique) to balance the dataset and ensure the model is well-trained on both classes. 2. Exploratory Data Analysis Correlation Analysis: Examined correlations between features to understand relationships and their potential impact on the model. 3. Model Building Algorithm Used: Random Forest Classifier, chosen for its robustness and high performance. Hyperparameter Tuning: Employed RandomizedSearchCV to find the best hyperparameters and enhance model accuracy. 4. Model Evaluation Confusion Matrix & Classification Report: Evaluated the model’s performance using key metrics such as precision, recall, F1-score, and overall accuracy. Feature Importance: Analyzed feature importances to identify which features contribute most to detecting fraud. Results The model achieved outstanding performance metrics:

Accuracy: 100% Precision, Recall, F1-score: 1.00 for both classes Confusion Matrix: True Negatives (TN): 9906 False Positives (FP): 8 False Negatives (FN): 9 True Positives (TP): 9757 Conclusion This project demonstrates the effectiveness of machine learning in detecting fraudulent credit card transactions. The key steps, including data preprocessing, handling class imbalance, and hyperparameter tuning, were crucial in achieving high model performance. The feature importance analysis provided valuable insights into the key indicators of fraudulent activity.

Check out the full code and detailed analysis in the GitHub Repository.

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AI And Machine Learning In Business Market Size 2025-2029

The AI and machine learning in business market size is valued to increase by USD 240.3 billion, at a CAGR of 24.9% from 2024 to 2029. Unprecedented advancements in AI technology and generative AI catalyst will drive the ai and machine learning in business market.

Major Market Trends & Insights

North America dominated the market and accounted for a 36% growth during the forecast period.
By Component - Solutions segment was valued at USD 24.98 billion in 2023
By Sector - Large enterprises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 906.25 million
Market Future Opportunities: USD 240301.30 million
CAGR from 2024 to 2029 : 24.9%

Market Summary

In the realm of business innovation, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as indispensable tools, shaping industries through unprecedented advancements. The market for AI in business is experiencing a surge in growth, with an estimated 1.2 billion dollars invested in AI startups in 2020 alone. This investment fuels the proliferation of generative AI copilots and embedded AI in enterprise platforms, revolutionizing processes and enhancing productivity. However, the integration of AI and ML in businesses presents a unique challenge: the scarcity of specialized talent.
As these technologies become increasingly essential, companies are compelled to invest in workforce transformation, upskilling their employees or hiring new talent to ensure they can harness the full potential of AI. This imperative for human capital development is a testament to the transformative power of AI and ML in business, driving growth and innovation across industries.

What will be the Size of the AI And Machine Learning In Business Market during the forecast period?

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How is the AI And Machine Learning In Business Market Segmented ?

The AI and machine learning in business industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Solutions
  Services


Sector

  Large enterprises
  SMEs


Application

  Data analytics
  Predictive analytics
  Cyber security
  Supply chain and inventory management
  Others


End-user

  IT and telecom
  BFSI
  Retail and manufacturing
  Healthcare
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    Australia
    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Component Insights

The solutions segment is estimated to witness significant growth during the forecast period.

The market continues to evolve, driven by advancements in big data processing, algorithm performance metrics, and scalable infrastructure. API integrations, recommendation engines, and predictive analytics tools are increasingly common, with model training datasets becoming larger and more diverse. Business process automation relies on feature engineering processes, data mining techniques, and model deployment strategies. Cloud computing platforms facilitate the use of deep learning algorithms, machine learning models, and real-time data processing. In 2023, SAP introduced Joule, an AI copilot that uses natural language processing for proactive and contextualized insights, reflecting the trend towards AI-driven automation and process optimization. This includes supply chain optimization, sales forecasting models, sentiment analysis tools, and anomaly detection systems.

Furthermore, AI-powered chatbots, data visualization dashboards, and model explainability techniques support data governance frameworks. Cybersecurity protocols and fraud detection models are also essential components of this dynamic landscape. According to a recent report, the global AI in business market is projected to reach USD267 billion by 2027, underscoring its transformative impact on industries.

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The Solutions segment was valued at USD 24.98 billion in 2019 and showed a gradual increase during the forecast period.

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Regional Analysis

North America is estimated to contribute 36% to the growth of the global market during the forecast period.Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The artificial intelligence (AI) and machine learning (ML) in business market is experiencing a significant surge, with North America leading the charge. The region, particularly the United States, h