Synthetic Data Generation Market Size

As per the latest insights from Market.us, the Global Synthetic Data Generation Market is set to reach USD 6,637.98 million by 2034, expanding at a CAGR of 35.7% from 2025 to 2034. The market, valued at USD 313.50 million in 2024, is witnessing rapid growth due to rising demand for high-quality, privacy-compliant, and AI-driven data solutions.

North America dominated in 2024, securing over 35% of the market, with revenues surpassing USD 109.7 million. The region’s leadership is fueled by strong investments in artificial intelligence, machine learning, and data security across industries such as healthcare, finance, and autonomous systems. With increasing reliance on synthetic data to enhance AI model training and reduce data privacy risks, the market is poised for significant expansion in the coming years.

The Synthetic Data Generation Marketsize was valued at USD 288.5 USD Million in 2023 and is projected to reach USD 1920.28 USD Million by 2032, exhibiting a CAGR of 31.1 % during the forecast period.Synthetic data generation stands for the generation of fake datasets that resemble real datasets with reference to their data distribution and patterns. It refers to the process of creating synthetic data points utilizing algorithms or models instead of conducting observations or surveys. There is one of its core advantages: it can maintain the statistical characteristics of the original data and remove the privacy risk of using real data. Further, with synthetic data, there is no limitation to how much data can be created, and hence, it can be used for extensive testing and training of machine learning models, unlike the case with conventional data, which may be highly regulated or limited in availability. It also helps in the generation of datasets that are comprehensive and include many examples of specific situations or contexts that may occur in practice for improving the AI system’s performance. The use of SDG significantly shortens the process of the development cycle, requiring less time and effort for data collection as well as annotation. It basically allows researchers and developers to be highly efficient in their discovery and development in specific domains like healthcare, finance, etc. Key drivers for this market are: Growing Demand for Data Privacy and Security to Fuel Market Growth. Potential restraints include: Lack of Data Accuracy and Realism Hinders Market Growth. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

Context

Getting proper data for survival analysis is often difficult.

Content

This data represents entry dates, departure dates and other information about fictional clients of a life insurance company. You have the age at which the insured entered the contract, the age at which he left, and the reason : either death or withdrawal, equivalent for us to right-censorship since the actual age at death of the person will no longer be observed. The data are left-truncated at the 1st of January 1820 : you only know if a client was present before that date, but you have no idea for how long he's been there.

Acknowledgements

Entirely generated using the numpy.random module, source code attached. For the survival analysis notebooks to come, my theoretical basis is the excellent course of Duration Models by Olivier Lopez at ENSAE Paris.

Inspiration

Develop some survival analysis and duration models tools to estimate death or departure of your clients as accurately as possible !

This dataset contains realistic synthetic data generated with a commercial tool, taking as an input a real dataset of CaixaBank’s express loans for a timespan of 18 months. The real dataset was tagged in order to identify the confirmed and tentative fraud cases in which a fraudster has impersonate the client to claim that type of loan and steal client’s funds. The dataset includes several indicators that help fraud analysts to identify any suspicious behaviour of the user that could imply an impersonation or misbehaviour. This dataset was used in INFINITECH H2020 project to build an AI model for cyberfraud prevention in this type of operations, which are especially critical because of two factors. First, it is type of loan, an operation in which the fraudster can steal money that the client does not really own, so it can be stolen even from clients without funds on their accounts. Second, it is an operation that was offered to the clients to speed up the process of acquiring loans of small amounts. The fraudsters can take profit of that and proceed faster as well stealing that money. The detail of the data fields included in the dataset is specified in the table below.

Field name

Value example

Field description

Fraud

0

Indicates if a fraud was produced in the operation. (0 No; 1 Intent of fraud; 2 Completed fraud -money stolen-)

PK_ANYOMES

202102

Year and month of the loan constitution operation

PK_ANYOMESDIA

20210207

Day of the loan constitution operation

PK_TSINSERCION

06:28,0

Time of the loan constitution operation

IDE_USUCLO_ORIG

1321946400

User associated with the online banking contract and the client. It is an internal user ID which is used jointly with PK_CONTRATO to access the services under the online banking contract.

PK_CONTRATO

1096097250023219464

online banking contract code. It is the identifier of the online banking services.

FK_NUMPERSO

27388223

Unique ID that identifies the physical person (client) who is connecting to online banking

IDE_SAU

08875268

Identifier used by the client to access online banking. This identifier is used jointly with CARPETA id to access online banking services.

CARPETA

49830679

Folder the online banking services of the clients are stored. It is used jointly with the client's online banking identifier (ID_SAU).

FK_COD_OPERACION

03693

Loan constitution transaction code. Unique ID that identifies the loan.

DES_OPERACION

CONSTITUCION PRESTAMO

Description of the loan constitution operation.

IP_TERMINAL

AAHUAWPOTLXYxgaNLC zWp70Yp+MaW2i1qEkh0o=

IP of the terminal or hash of the mobile device from which the client connects to online banking.

FK_NUMPERSO_TIT_LOE

27388223

Identifier of the physical person that is the online banking contract holder. It can be different to FK_NUMPERSO, if FK_NUMPERSO is an authorised person to operate the online banking services of FK_NUMPERSO_TIT_LOE. It can happen both for FK_NUMPERSO_TIT_LOE representing physical or legal persons (enterprises).

FK_CONTRATO_PPAL_OPE

1001037520210005473

Contract code of the savings account in which the loan is deposited. This is not the same contract as the online banking contract.

FK_IMPORTE_PRINCIPAL

1500

Loan amount demanded.

IND_MFA_OPE

0

Indicator of the response of the SCA (Strong Customer Authentication) request decision algorithm for the loan consolidation operation. (0 No; 1 Yes; -1 Unknown)

MESSAGE_MFA_OPE

Konline bankingN USER AND DEVICE

SCA (Strong Customer Authentication) request decision algorithm response message for loan consolidation operation.

SALDO_ANTES_PRESTAMO

100

Balance of the account into which the loan is deposited just before the loan.

POSICION_GLOBAL_ANTES_PRESTAMO

1

Global balance of the client before the loan. (1: <1000; 2: 1000-10000; 3: 10000-50000; 4: 50000-250000; 5: >250000; -2: Data not found)

IND_NUEVO_IDE_SAU

0

If the identifier used to access online banking has been created in the last 48 hours. (0 No; 1 Yes; -1 Unknown)

FECHA_ALTA_CLIENTE

39246

Indicate the date of registration with CaixaBank as a customer. When the physical person (FK_NUMPERSO) became a client of CaixaBank

IND_ALTA_SIGN

0

Indicates if the client has registered a sign in the last 48 hours. (0 No; 1 Yes; -1 Unknown)

IND_GMP_ANT

0

Indicates if there has been a new primary mobile assignment in the 48 hours prior to the loan. (0 No; 1 Yes; -1 Unknown)

IND_INGRESO_NOMINA

1

Indicate if the payroll of FK_NUMPERSO is domiciled at CaixaBank. (0 No; 1 Yes)

IND_PENSION

0

Indicate if FK_NUMPERSO has the pension domiciled in CaixaBank. (0 No; 1 Yes)

IND_IMAGIN_BANK

1

Indicate if FK_NUMPERSO is ImaginBank customer (0 No; 1 Yes)

IND_EXTRANJERO

0

Indicate if FK_NUMPERSO is a foreigner (0 National; 1 Foreigner)

IND_RESIDENTE

1

Indicate if FK_NUMPERSO resides in Spain (0 No; 1 Yes)

FK_TIPREL

1

Type of the ownership of the savings account in which the loan is deposited (values between 1 and 48). 1 means it is an account holder. Other values mean other type of relationships (i.e. "authorized person but not an owner of the account").

FK_ORDREL

1

Order of the ownership relationship. If there are more than account holder, in which position is the FK_NUMPERSO.

Dataset

This dataset was created by Rohit Debnath

Released under CC0: Public Domain

Contents

Graph and download economic data for Quarterly Financial Report: U.S. Corporations: Basic Chemicals, Resins, and Synthetics: Income (Loss) Before Income Taxes (QFR111375USNO) from Q4 2000 to Q4 2024 about gains/losses, resin, synthetic, chemicals, finance, nondurable goods, tax, corporate, goods, income, manufacturing, industry, and USA.

AnonymousLLMer/finance-corpus-synthetic dataset hosted on Hugging Face and contributed by the HF Datasets community

The rapid evolution of machine learning (ML) offers transformative potential for the credit scoring industry, especially in addressing the challenges faced by "thin-file" consumers who lack substantial credit histories. Traditional credit scoring models often fail to accurately assess these consumers due to insufficient data, leading to potential exclusion from crucial credit services. This research leverages a synthetically created dataset, generated using advanced Python libraries like Pandas, NumPy, and Faker, to develop and refine ML algorithms capable of evaluating such underserved consumer segments. The synthetic nature of the dataset ensures compliance with privacy norms while allowing the simulation of diverse consumer behaviors—from stable to erratic financial activities—typical of thin-file profiles. This initiative not only drives innovation in algorithmic credit scoring but also aligns with broader objectives of financial inclusivity, aiming to bridge service gaps by equipping the financial industry with tools to fairly evaluate creditworthiness across all consumer segments. Thus, this dataset forms a critical cornerstone for advancing research that enhances technical capabilities and fosters societal progress through improved financial inclusion.

The data collection and labeling market is experiencing robust growth, fueled by the escalating demand for high-quality training data in artificial intelligence (AI) and machine learning (ML) applications. The market, estimated at $15 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 25% over the forecast period (2025-2033), reaching approximately $75 billion by 2033. This expansion is primarily driven by the increasing adoption of AI across diverse sectors, including healthcare (medical image analysis, drug discovery), automotive (autonomous driving systems), finance (fraud detection, risk assessment), and retail (personalized recommendations, inventory management). The rising complexity of AI models and the need for more diverse and nuanced datasets are significant contributing factors to this growth. Furthermore, advancements in data annotation tools and techniques, such as active learning and synthetic data generation, are streamlining the data labeling process and making it more cost-effective. However, challenges remain. Data privacy concerns and regulations like GDPR necessitate robust data security measures, adding to the cost and complexity of data collection and labeling. The shortage of skilled data annotators also hinders market growth, necessitating investments in training and upskilling programs. Despite these restraints, the market’s inherent potential, coupled with ongoing technological advancements and increased industry investments, ensures sustained expansion in the coming years. Geographic distribution shows strong concentration in North America and Europe initially, but Asia-Pacific is poised for rapid growth due to increasing AI adoption and the availability of a large workforce. This makes strategic partnerships and global expansion crucial for market players aiming for long-term success.

Graph and download economic data for Quarterly Financial Report: U.S. Corporations: Basic Chemicals, Resins, and Synthetics: Provision for Current and Deferred Domestic Income Taxes (QFRD114375USNO) from Q4 2000 to Q3 2024 about deferred, resin, synthetic, chemicals, finance, nondurable goods, tax, domestic, corporate, goods, income, manufacturing, industry, and USA.

Graph and download economic data for Quarterly Financial Report: U.S. Corporations: Basic Chemicals, Resins, and Synthetics: Depreciation, Depletion, and Amortization of Property, Plant, and Equipment (QFR102375USNO) from Q4 2000 to Q4 2024 about amortization, depreciation, plant, resin, synthetic, chemicals, finance, nondurable goods, equipment, corporate, goods, manufacturing, industry, and USA.

Japan Imports: CS: EOP: Synthetic Perfume data was reported at 4.151 JPY bn in Jun 2018. This records a decrease from the previous number of 4.534 JPY bn for May 2018. Japan Imports: CS: EOP: Synthetic Perfume data is updated monthly, averaging 3.058 JPY bn from Jan 2009 (Median) to Jun 2018, with 114 observations. The data reached an all-time high of 4.817 JPY bn in Jun 2014 and a record low of 1.699 JPY bn in Mar 2009. Japan Imports: CS: EOP: Synthetic Perfume data remains active status in CEIC and is reported by Ministry of Finance. The data is categorized under Global Database’s Japan – Table JP.JA042: Imports by Commodity: Value.

Japan Imports: Vol: MG: TYF: Synthetic Fabrics, Woven data was reported at 5,362.343 kg th in Jun 2018. This records a decrease from the previous number of 6,437.120 kg th for May 2018. Japan Imports: Vol: MG: TYF: Synthetic Fabrics, Woven data is updated monthly, averaging 5,430.171 kg th from Jan 2009 (Median) to Jun 2018, with 114 observations. The data reached an all-time high of 6,472.494 kg th in Jan 2015 and a record low of 3,607.691 kg th in Feb 2009. Japan Imports: Vol: MG: TYF: Synthetic Fabrics, Woven data remains active status in CEIC and is reported by Ministry of Finance. The data is categorized under Global Database’s Japan – Table JP.JA043: Imports by Commodity: Volume.

Japan Exports Unit Value Index: FF: Synthetic Fabrics data was reported at 104.960 2000=100 in Sep 2008. This records a decrease from the previous number of 114.350 2000=100 for Aug 2008. Japan Exports Unit Value Index: FF: Synthetic Fabrics data is updated monthly, averaging 101.250 2000=100 from Jan 1998 (Median) to Sep 2008, with 129 observations. The data reached an all-time high of 124.000 2000=100 in Jan 1998 and a record low of 91.600 2000=100 in Oct 2003. Japan Exports Unit Value Index: FF: Synthetic Fabrics data remains active status in CEIC and is reported by Ministry of Finance. The data is categorized under Global Database’s Japan – Table JP.JA055: Exports Unit Value Index: 2000=100.

Bitext - Retail Banking Tagged Training Dataset for LLM-based Virtual Assistants

  Overview

This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Retail Banking] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-retail-banking-llm-chatbot-training-dataset.

86% of Europeans say that they feel confident in managing their personal finances, and 73% feel confident with banking online. However, the results vary across Member States, gender, age, and level of education – showing the need for continued attention to financial literacy. Europeans also care about sustainable finance but lack usable information about it.

Processed data

Processed data files for the Eurobarometer surveys are published in .xlsx format.

• Volume A "Countries/EU" The file contains frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of (weighted) replies for each country or territory and for (weighted) EU results.
• Volume AP "Trends" The file compares to previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume A and for (weighted) results.
• Volume AA "Groups of countries" The file contains (labelled) frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of (weighted) replies for groups of countries specified by the managing unit on the part of the EC.
• Volume AAP "Trends of groups of countries" The file contains shifts compared to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each groups of countries foreseen in Volume AA and for (weighted) results.
• Volume B "EU/socio-demographics" The file contains (labelled) frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies for the EU as a whole (weighted) and cross-tabulated by some 20 sociodemographic, socio-political or other variables, depending on the request from the managing unit on the part of the EC or the managing department of the other contracting authorities.
• Volume BP "Trends of EU/socio-demographics" The file contains shifts compared to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume B above)and for (weighted) results.
• Volume C "Country/socio-demographics" The file contains (labelled) weighted frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies for each country or territory surveyed separately and cross-tabulated by some 20 socio-demographic, socio-political or other variables (including a regional breakdown).

For SPSS files and questionnaires, please contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

Graph and download economic data for Quarterly Financial Report: U.S. Corporations: Basic Chemicals, Resins, and Synthetics: Retained Earnings at Beginning of Quarter (QFRD119375USNO) from Q4 2000 to Q4 2024 about retained earnings, resin, synthetic, chemicals, finance, nondurable goods, earnings, corporate, goods, manufacturing, industry, and USA.

Japan Export Value Index: Synthetic Fabrics data was reported at 93.960 2015=100 in Jul 2018. This records a decrease from the previous number of 106.800 2015=100 for Jun 2018. Japan Export Value Index: Synthetic Fabrics data is updated monthly, averaging 97.820 2015=100 from Jan 2003 (Median) to Jul 2018, with 187 observations. The data reached an all-time high of 149.430 2015=100 in Dec 2006 and a record low of 52.930 2015=100 in Jan 2011. Japan Export Value Index: Synthetic Fabrics data remains active status in CEIC and is reported by Ministry of Finance. The data is categorized under Global Database’s Japan – Table JP.JA064: Exports Value Index: 2015=100.

Graph and download economic data for Quarterly Financial Report: U.S. Corporations: Basic Chemicals, Resins, and Synthetics: Stockholders' Equity (QFR327375USNO) from Q4 2000 to Q4 2024 about resin, synthetic, equity, chemicals, finance, nondurable goods, corporate, goods, manufacturing, industry, and USA.

Japan Exports: CS: DTC: Synthetic Organic Dyestuff data was reported at 4.350 JPY bn in Sep 2018. This records a decrease from the previous number of 4.646 JPY bn for Aug 2018. Japan Exports: CS: DTC: Synthetic Organic Dyestuff data is updated monthly, averaging 3.894 JPY bn from Jan 2009 (Median) to Sep 2018, with 117 observations. The data reached an all-time high of 5.307 JPY bn in Apr 2011 and a record low of 2.159 JPY bn in Jan 2009. Japan Exports: CS: DTC: Synthetic Organic Dyestuff data remains active status in CEIC and is reported by Ministry of Finance. The data is categorized under Global Database’s Japan – Table JP.JA026: Exports by Commodity: Value.