This dataset contains large-scale user reviews collected from the Google Play Store for five leading mobile banking applications in Türkiye: İşbank (İşCep), YapıKredi, Garanti BBVA, Akbank, and Ziraat Bank. The dataset includes more than 250000 user reviews, covering multiple dimensions of user experience such as satisfaction, complaints, feature requests, and performance feedback.

Each record provides detailed information, including:

• package_name (unique identifier of the mobile banking app)

• review_id (unique review identifier)

• user_name (anonymized or pseudonymized user name)

• content (review text)

• score (star rating, 1–5)

• thumbs_up_count (number of likes/upvotes)

• app_version and review_created_version

• timestamps (UTC and Istanbul local time)

• bank_name (associated financial institution)

The dataset was collected in August 2025 using the Google Play Scraper library, ensuring systematic extraction of publicly available app store data. All reviews are provided in Turkish (scrape_lang = "tr"), with precise timestamps for temporal analysis.

This dataset can support research in:

• User experience analysis in digital banking

• Sentiment analysis and opinion mining

• Topic modeling and service quality evaluation

• Time series forecasting of user satisfaction trends

• Comparative studies across multiple financial institutions

Researchers, practitioners, and developers can use this dataset to explore trends in digital banking adoption, analyze service quality signals, and develop machine learning models for predicting user satisfaction in mobile financial technologies.

Big Data Analytics in Banking Market Outlook

The Big Data Analytics in Banking market size was valued at approximately USD 23.5 billion in 2023, and it is projected to grow to USD 67.2 billion by 2032, showcasing a robust CAGR of 12.3%. This exponential growth is driven by the increasing demand for more refined data analysis tools that enable banks to manage vast amounts of information and derive actionable insights. The banking sector is increasingly acknowledging the need for advanced analytics to enhance decision-making processes, improve customer satisfaction, and mitigate risks. Factors such as digital transformation, regulatory pressure, and the need for operational efficiency continue to propel the market forward.

One of the primary growth factors in the Big Data Analytics in Banking market is the heightened emphasis on risk management. Banks are continuously exposed to various risks, including credit, market, operational, and liquidity risks. Big Data Analytics plays a crucial role in identifying, measuring, and mitigating these risks. By analyzing large volumes of structured and unstructured data, banks can gain insights into potential risk factors and develop strategies to address them proactively. Furthermore, regulatory requirements mandating more stringent risk management practices have compelled banks to invest in sophisticated analytics solutions, further contributing to market growth.

Another significant driver of this market is the increasing need for enhanced customer analytics. With the rise of digital banking and fintech solutions, customers now demand more personalized services and experiences. Big Data Analytics enables banks to understand customer behavior, preferences, and needs by analyzing transaction histories, social media interactions, and other data sources. By leveraging these insights, banks can offer tailored products and services, improve customer retention rates, and gain a competitive edge in the market. Additionally, customer analytics helps banks identify cross-selling and up-selling opportunities, thereby driving revenue growth.

Fraud detection is also a critical area where Big Data Analytics has made a significant impact in the banking sector. The increasing complexity and frequency of financial frauds necessitate the adoption of advanced analytics solutions to detect and prevent fraudulent activities effectively. Big Data Analytics allows banks to analyze vast amounts of transaction data in real-time, identify anomalies, and flag suspicious activities. By employing machine learning algorithms, banks can continuously improve their fraud detection capabilities, minimizing financial losses and enhancing security for their customers. This ongoing investment in fraud detection tools is expected to contribute significantly to the growth of the Big Data Analytics in Banking market.

Data Analytics In Financial services is revolutionizing the way banks operate by providing deeper insights into financial trends and customer behaviors. This transformative approach enables financial institutions to analyze vast datasets, uncovering patterns and correlations that were previously inaccessible. By leveraging data analytics, banks can enhance their financial forecasting, optimize asset management, and improve investment strategies. The integration of data analytics in financial operations not only aids in risk assessment but also supports regulatory compliance by ensuring accurate and timely reporting. As the financial sector continues to evolve, the role of data analytics becomes increasingly pivotal in driving innovation and maintaining competitive advantage.

Regionally, North America remains a dominant player in the Big Data Analytics in Banking market, driven by the presence of major banking institutions and technology firms. The region's early adoption of advanced technologies and a strong focus on regulatory compliance have been pivotal in driving market growth. Europe follows closely, with stringent regulatory frameworks like GDPR necessitating advanced data management and analytics solutions. In the Asia Pacific region, rapid digital transformation and the growing adoption of mobile banking are key factors propelling the market forward. The Middle East & Africa and Latin America, while currently smaller markets, are experiencing steady growth as banks in these regions increasingly invest in analytics solutions to enhance their competitive positioning.

Component Analysis

In the Big Data Analytics in

Product Analytics for Digital Banking Market Outlook

According to our latest research, the global market size for Product Analytics for Digital Banking reached USD 2.74 billion in 2024, with a robust CAGR of 17.9% anticipated during the forecast period. By 2033, the market is projected to reach USD 9.42 billion, reflecting the accelerating adoption of advanced analytics tools in digital banking environments. The primary growth factor driving this expansion is the increasing demand for personalized customer experiences and data-driven decision-making, as financial institutions strive to differentiate themselves in a highly competitive, digital-first marketplace.

Several key growth drivers are propelling the Product Analytics for Digital Banking Market forward. The rapid digital transformation across the banking sector is compelling institutions to adopt sophisticated analytics solutions to understand and optimize every aspect of the customer journey. As customers increasingly interact with banks through digital channels, banks are leveraging analytics to capture granular data on user behaviors, preferences, and pain points. This wealth of data enables banks to tailor offerings, enhance user satisfaction, and drive higher engagement rates. Furthermore, the surge in mobile banking and the proliferation of digital payment platforms are generating vast datasets that require advanced analytics for actionable insights, thereby fueling market growth.

Another significant factor contributing to the marketÂ’s expansion is the growing emphasis on regulatory compliance and risk management. As financial regulations become more stringent, banks are turning to product analytics to ensure adherence to compliance standards and mitigate fraud risks. Analytics platforms help banks detect anomalies, monitor transaction patterns, and flag suspicious activities in real time, which is crucial for maintaining trust and security in digital banking operations. Additionally, the integration of artificial intelligence and machine learning in analytics solutions is enhancing predictive capabilities, allowing banks to anticipate customer needs, reduce churn, and proactively address operational inefficiencies.

The increasing focus on customer retention and lifetime value is also a pivotal growth driver for the Product Analytics for Digital Banking Market. Banks are utilizing analytics to segment customers, identify at-risk accounts, and implement targeted retention strategies. By analyzing feature adoption, engagement metrics, and conversion rates, banks can optimize product offerings and deliver personalized experiences that foster long-term loyalty. The shift from traditional banking to digital-only models, particularly among younger demographics, is intensifying the need for continuous innovation in analytics-driven customer engagement. Consequently, banks are investing heavily in analytics platforms to stay ahead of evolving customer expectations and competitive pressures.

In today's digital banking landscape, Multichannel Analytics is becoming increasingly vital as banks strive to provide a seamless customer experience across various platforms. By integrating data from multiple channels such as mobile apps, online banking, and in-branch interactions, banks can gain a holistic view of customer behavior. This comprehensive insight allows financial institutions to identify trends, optimize customer journeys, and deliver personalized services that meet the evolving needs of their clients. As customers engage with banks through diverse touchpoints, the ability to analyze and leverage this multichannel data becomes a key differentiator in enhancing customer satisfaction and loyalty.

From a regional perspective, North America currently leads the global Product Analytics for Digital Banking Market, accounting for the largest share in 2024. This dominance is attributed to the high adoption rate of digital banking technologies, a mature financial services ecosystem, and significant investments in analytics infrastructure. Europe and Asia Pacific are also witnessing substantial growth, with Asia Pacific expected to register the highest CAGR during the forecast period. The rapid digitalization of banking services in emerging economies, coupled with increasing smartphone penetration and favorable regulatory frameworks, is drivi

About Dataset

There has been a revenue decline in the Portuguese Bank and they would like to know what actions to take. After investigation, they found that the root cause was that their customers are not investing enough for long term deposits. So the bank would like to identify existing customers that have a higher chance to subscribe for a long term deposit and focus marketing efforts on such customers.

Data Set Information

The data is related to direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be subscribed ('yes') or not ('no') subscribed.

There are two datasets: train.csv with all examples (32950) and 21 inputs including the target feature, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al., 2014]

test.csv which is the test data that consists of 8238 observations and 20 features without the target feature

Goal:- The classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y).

The dataset contains train and test data. Features of train data are listed below. And the test data have already been preprocessed.

Features

Target variable (desired output):

Vector Databases for Banking AI Market Outlook

According to our latest research, the global market size for Vector Databases for Banking AI stood at USD 1.27 billion in 2024, reflecting the rapid adoption of advanced database solutions within the financial sector. The market is projected to expand at a robust CAGR of 23.9% from 2025 to 2033, reaching an estimated value of USD 10.85 billion by 2033. This exceptional growth is primarily driven by the increasing integration of artificial intelligence and machine learning technologies in banking operations, which necessitate efficient, scalable, and high-performance data storage and retrieval systems. The demand for real-time analytics, fraud detection, and personalized banking experiences are further catalyzing the adoption of vector databases across global banking institutions.

The surge in digital transformation initiatives within the banking sector is a key growth factor for the Vector Databases for Banking AI market. As banks strive to enhance operational efficiency, improve customer engagement, and streamline compliance processes, there is a growing reliance on AI-powered solutions that require robust data infrastructure. Vector databases, with their ability to handle high-dimensional data and support complex similarity searches, are being increasingly deployed to support use cases such as fraud detection, risk management, and customer analytics. The proliferation of digital payment systems, mobile banking, and online transactions has further amplified the need for scalable data platforms capable of processing vast volumes of unstructured and semi-structured data in real time, thereby fueling market expansion.

Another significant driver is the escalating sophistication of financial crimes and the corresponding need for advanced security measures. Banks and financial institutions are leveraging AI-driven vector databases to detect anomalies, identify suspicious patterns, and mitigate risks associated with fraudulent activities. These databases enable rapid analysis of transactional data, behavioral patterns, and network relationships, empowering banks to respond proactively to emerging threats. Moreover, regulatory pressures and compliance requirements are prompting banks to invest in technologies that can ensure data integrity, traceability, and transparency, all of which are facilitated by modern vector database architectures. The integration of vector databases with AI models not only enhances the accuracy of fraud detection but also reduces false positives, leading to improved operational outcomes.

The growing emphasis on personalized banking services and customer-centric strategies is also propelling the adoption of vector databases in the banking AI landscape. Financial institutions are increasingly utilizing AI algorithms to segment customers, predict needs, and deliver tailored product recommendations. Vector databases play a crucial role in enabling these capabilities by providing efficient storage and retrieval of high-dimensional customer data, facilitating real-time analytics, and supporting natural language processing applications. As competition intensifies in the banking industry, the ability to leverage data-driven insights for customer acquisition, retention, and cross-selling is becoming a key differentiator, thereby driving further investments in vector database technologies.

From a regional perspective, North America currently dominates the Vector Databases for Banking AI market, owing to the presence of leading financial institutions, advanced technological infrastructure, and a strong focus on innovation. Europe and Asia Pacific are also witnessing significant growth, driven by rising digitalization, increasing adoption of AI in banking, and favorable regulatory environments. Emerging markets in Latin America and the Middle East & Africa are gradually catching up, supported by government initiatives to promote financial inclusion and digital banking. While regional dynamics vary, the overarching trend is a global shift towards data-centric banking operations, underpinned by the adoption of next-generation database solutions.

Component Analysis

The Component segment of the Vector Databases for Banking AI market is bifurcated into Software and Services, each playing a pivotal role in shaping the market landscape. The software segment primarily includes vector database management systems, data indexing engines, and integrat

Time Series Database for Financial Services Market Outlook

As per our latest research, the global Time Series Database for Financial Services market size in 2024 reached USD 1.85 billion, demonstrating robust growth driven by the increasing adoption of real-time analytics and data-driven decision-making in the financial sector. The market is expected to expand at a CAGR of 13.2% from 2025 to 2033, reaching a forecasted value of USD 5.44 billion by 2033. The primary growth factor for this market is the escalating volume of financial transactions and the growing need for high-frequency data analysis, which is crucial for risk management, fraud detection, and algorithmic trading across global financial institutions.

One of the most significant growth drivers for the Time Series Database for Financial Services market is the exponential rise in digital transactions and the proliferation of fintech solutions. Financial institutions are increasingly leveraging time series databases to process and analyze vast streams of transactional data in real time. This capability is essential for supporting complex applications such as algorithmic trading, which relies on millisecond-level data precision to execute trades and manage portfolios efficiently. The surge in mobile banking, online payments, and digital wallets has further amplified the demand for scalable and high-performance databases that can handle the velocity, volume, and variety of financial data generated every second. As financial services become more digitized, the need for robust data infrastructure continues to intensify, propelling the market forward.

Another critical factor fueling market growth is the regulatory environment and the increasing emphasis on compliance and risk management. Financial institutions are under mounting pressure to comply with stringent regulations imposed by global authorities, which necessitate comprehensive data tracking, auditing, and reporting capabilities. Time series databases offer an efficient way to store and retrieve historical data, making it easier for banks, investment firms, and insurance companies to demonstrate compliance and quickly respond to regulatory inquiries. Moreover, the integration of advanced analytics and artificial intelligence with time series databases enables organizations to detect anomalies, predict risks, and automate compliance workflows, thereby reducing operational costs and mitigating potential penalties.

Technological advancements and the rise of cloud computing are also pivotal in shaping the growth trajectory of the Time Series Database for Financial Services market. Cloud-based deployment models have democratized access to high-performance databases, enabling even small and medium-sized enterprises to leverage sophisticated data management capabilities without significant upfront investments. The scalability, flexibility, and cost-efficiency offered by cloud solutions are attracting a diverse range of financial service providers, from traditional banks to innovative fintech startups. Furthermore, the integration of time series databases with big data platforms and machine learning tools is unlocking new opportunities for real-time analytics, personalized financial services, and predictive modeling, all of which contribute to the sustained expansion of the market.

From a regional perspective, North America continues to dominate the global Time Series Database for Financial Services market, accounting for the largest revenue share in 2024. This leadership position is attributed to the presence of major financial hubs, advanced IT infrastructure, and early adoption of cutting-edge technologies by leading banks and investment firms. However, the Asia Pacific region is emerging as the fastest-growing market, driven by rapid digital transformation, increasing investments in fintech, and the rising adoption of cloud-based solutions in countries such as China, India, and Singapore. Europe is also witnessing substantial growth, supported by stringent regulatory frameworks and the increasing focus on data-driven financial services. Latin America and the Middle East & Africa are gradually catching up, with financial institutions in these regions investing in modern database solutions to enhance operational efficiency and customer experience.

In the evolving landscape of financial services, <a href="https://growthmarketreports.com/report/managed-temporal-services-market" target="_blank&

Data Set Information

This data set contains records relevant to a direct marketing campaign of a Portuguese banking institution. The marketing campaign was executed through phone calls. Often, more than one call needs to be made to a single client before they either decline or agree to a term deposit subscription. The classification goal is to predict if the client will subscribe (yes/no) to the term deposit (variable y).

This is a modified version of the classic bank marketing data set originally shared in the UCI Machine Learning Repository. There are four datasets available on UCI's repository: 1) bank-additional-full.csv with all examples (41188) and 20 inputs, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al., 2014] 2) bank-additional.csv with 10% of the examples (4119), randomly selected from 1), and 20 inputs. 3) bank-full.csv with all examples and 17 inputs, ordered by date (older version of this data set with less inputs). 4) bank.csv with 10% of the examples and 17 inputs, randomly selected from 3 (older version of this data set with less inputs). Note: The smallest datasets are provided to test more computationally demanding machine learning algorithms (e.g., SVM).

This data set is a copy of data set no. 1 (bank-additional-full.csv) from the list above with one input feature (representing duration of phone call) removed. The following is a note from the variable description in the original data set:

duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.

The duration feature is excluded in this data set to prevent data leakage.

Contents

Input variables:

bank client data: 1 - age (numeric) 2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown') 3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed) 4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown') 5 - default: has credit in default? (categorical: 'no','yes','unknown') 6 - housing: has housing loan? (categorical: 'no','yes','unknown') 7 - loan: has personal loan? (categorical: 'no','yes','unknown')

related with the last contact of the current campaign: 8 - contact: contact communication type (categorical: 'cellular','telephone') 9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec') 10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri')

other attributes: 11 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 12 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted) 13 - previous: number of contacts performed before this campaign and for this client (numeric) 14 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')

social and economic context attributes: 15 - emp.var.rate: employment variation rate - quarterly indicator (numeric) 16 - cons.price.idx: consumer price index - monthly indicator (numeric) 17 - cons.conf.idx: consumer confidence index - monthly indicator (numeric) 18 - euribor3m: euribor 3 month rate - daily indicator (numeric) 19 - nr.employed: number of employees - quarterly indicator (numeric)

Output variable (desired target):

20 - y - has the client subscribed a term deposit? (binary: 'yes','no')

Acknowledgements

Source: [Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014

Data credit goes to UCI. Visit their website to access the original data set directly: https://archive.ics.uci.edu/ml/datasets/Bank%2BMarketing

Context

Use this data set to test the performance of your classification models and to explore the best strategies to improve a banking institution's next direct marketing campaign.

Term deposits are cash investment held at a financial institution and are a major source of revenue for banks--making them important for financial institutions to market. Telemarketing remains to be a popular marketing technique beca...

Cash-and-Equivalents Time Series for Park National Corporation. Park National Corporation operates as the bank holding company for Park National Bank that provides commercial banking and trust services in small and medium population areas in the United States. The company offers deposits for demand, savings, and time accounts; trust and wealth management services; cash management services; safe deposit operations; electronic funds transfers; Internet and mobile banking solutions with bill pay service; credit cards; and various additional banking-related services. It also provides commercial loans, including financing for industrial and commercial properties, financing for equipment, inventory and accounts receivable, acquisition financing, and commercial leasing, as well as for consumer finance companies; commercial real estate loans comprising mortgage loans to developers and owners of commercial real estate; originates financing leases primarily for the purchase of commercial vehicles, operating/manufacturing equipment, and municipal vehicles/equipment; consumer loans, such as automobile loans; consumer finance services; home equity lines of credit; and residential real estate and construction loans, as well as installment loans and commercial loans. In addition, the company offers aircraft financing services; and ParkDirect, a personal banking application. Park National Corporation was founded in 1908 and is headquartered in Newark, Ohio.

Net-Income Time Series for Digital China Information Service Co Ltd. Digital China Information Service Group Company Ltd. provides financial technology products and solutions in China and internationally. The company offers consulting, software products, fintech solution implementation, and cloud infrastructure services. It also provides financial software products comprising Sm@rtOneBank, a banking solution that addresses core banking, general ledger, payment, trade finance, credit management, counter system, e-banking, mobile banking, and ECIF requirements of universal, retail, corporate, and digital banks; Sm@rtGalaxy4.0, a cloud native financial PaaS platform used for ecological support for the construction of middle offices, including operation, maintenance, development, and management; Sm@rtGL, a transaction-grade general ledger system featuring concurrency, data volume, and requiring efficiency and flexibility; Sm@rtEMSP, an enterprise microservice platform that serves as a financial architecture software solution for the financial industry offering extensive and flexible reusable capabilities, concentrated/centralized management of basic platform components, and unified technical capability; Sm@rtEnsemble, a core banking system that serves as a banking business processing system; and Sm@rtTeller X, an integrated smart counter system used for human-machine interaction and business scenario capabilities. In addition, the company offers computer system integration; surveying and mapping; software, hardware, and technology development; network optimization; investment management; information technology; and technical services, as well as sells financial equipment. It serves financial institutions, regional institutions, banks, and fintech providers and partners. The company was formerly known as Digital China Information Service Company Ltd. and changed its name to Digital China Information Service Group Company Ltd. in August 2023. Digital China Information Service Group Company Ltd. is headquartered in Beijing, the People's Republic of China.

India Mobile Banking Transactions: Volume data was reported at 17,117.198 Unit mn in Mar 2025. This records an increase from the previous number of 15,003.146 Unit mn for Feb 2025. India Mobile Banking Transactions: Volume data is updated monthly, averaging 245.260 Unit mn from Apr 2011 (Median) to Mar 2025, with 168 observations. The data reached an all-time high of 17,117.198 Unit mn in Mar 2025 and a record low of 1.080 Unit mn in Apr 2011. India Mobile Banking Transactions: Volume data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Monetary – Table IN.KAI017: Mobile Payments. [COVID-19-IMPACT]

1) Data Introduction • The Direct Marketing Campaigns (Bank Marketing) Dataset is a dataset built to predict time deposits (deposit) based on customer characteristics and campaign history in Portuguese banks' phone-based direct marketing campaigns.

2) Data Utilization (1) Direct Marketing Campaigns (Bank Marketing) Dataset has characteristics that: • Consisting of 41,188 rows, individual case data for calls made to customers during each row marketing campaign. • This dataset contains 21 columns (characteristics) that provide detailed information about each phone and attributes related to customers and campaigns. (2) Direct Marketing Campaigns (Bank Marketing) Dataset can be used to: • Marketing Campaign Performance Forecasting and Customer Targeting: Using customer characteristics and historical campaign data, it can be used to predict customers who are likely to sign up for time deposits and to establish effective marketing targeting strategies. • Customer Behavior Analysis and Marketing Strategy Optimization: You can optimize marketing strategies by analyzing campaign response patterns, characteristics by customer group, and correlations with economic indicators, and use them for customer segmentation and customized product suggestions.

India Mobile Banking Transactions: Value data was reported at 37,696,017.240 INR mn in Mar 2025. This records an increase from the previous number of 32,155,172.112 INR mn for Feb 2025. India Mobile Banking Transactions: Value data is updated monthly, averaging 1,798,543.365 INR mn from Apr 2011 (Median) to Mar 2025, with 168 observations. The data reached an all-time high of 37,696,017.240 INR mn in Mar 2025 and a record low of 760.000 INR mn in Apr 2011. India Mobile Banking Transactions: Value data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Monetary – Table IN.KAI017: Mobile Payments. [COVID-19-IMPACT]

This dataset contains the raw survey responses and the PLS-SEM output used in a study examining the factors influencing the continued use of mobile banking services in Indonesia. A total of 509 responses were collected through an online questionnaire using a convenience sampling method. The constructs were based on the UTAUT and Status Quo Bias models within the Dual Factor Theory (DFT) framework. The dataset includes both the original response data and the structural model output files used for hypothesis testing.

Context

Data set is taken from here. The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution.

Columns

1 - age (numeric) 2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown') 3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed) 4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown') 5 - default: has credit in default? (categorical: 'no','yes','unknown') 6 - housing: has housing loan? (categorical: 'no','yes','unknown') 7 - loan: has personal loan? (categorical: 'no','yes','unknown')

Related with the last contact of the current campaign:

8 - contact: contact communication type (categorical: 'cellular','telephone') 9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec') 10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri') 11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.

Other attributes:

12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted) 14 - previous: number of contacts performed before this campaign and for this client (numeric) 15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')

Social and economic context attributes

16 - emp.var.rate: employment variation rate - quarterly indicator (numeric) 17 - cons.price.idx: consumer price index - monthly indicator (numeric) 18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric) 19 - euribor3m: euribor 3 month rate - daily indicator (numeric) 20 - nr.employed: number of employees - quarterly indicator (numeric) 21 - subscribed : has the client subscribed a term deposit? (binary: 'yes','no')

Citation

[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014

CompanyData.com (BoldData) provides accurate, verified business intelligence sourced directly from official trade registers and financial authorities. Our global database includes 1 million banking and insurance companies, giving you unrivaled access to financial institutions, commercial banks, fintech firms, life insurers, reinsurers, and investment companies across every major market.

Each record in our database is enriched with high-value details such as company hierarchies, executive contacts, email addresses, direct phone numbers, mobile numbers, industry codes, and firmographic data including company size, revenue, and location. This ensures you get not just quantity, but precision and relevance for your business needs. Our data is continually verified and updated to meet the strictest accuracy and compliance standards.

Organizations worldwide use our financial services dataset for a wide range of applications—from regulatory compliance and KYC verification, to financial services sales outreach, marketing campaigns, CRM or ERP database enrichment, and AI training models. Whether you're targeting insurance providers in Europe or identifying investment firms in Asia, our dataset provides the clarity and coverage to move forward with confidence.

You can access the data through custom-tailored bulk downloads, real-time API integrations, or explore and filter companies directly through our easy-to-use self-service platform. With a total coverage of 380 million verified companies globally, CompanyData.com (BoldData) is your trusted partner for navigating the complex and regulated landscape of global finance and insurance.

Debt-To-Assets-Ratio Time Series for Digital China Information Service Co Ltd. Digital China Information Service Group Company Ltd. provides financial technology products and solutions in China and internationally. The company offers consulting, software products, fintech solution implementation, and cloud infrastructure services. It also provides financial software products comprising Sm@rtOneBank, a banking solution that addresses core banking, general ledger, payment, trade finance, credit management, counter system, e-banking, mobile banking, and ECIF requirements of universal, retail, corporate, and digital banks; Sm@rtGalaxy4.0, a cloud native financial PaaS platform used for ecological support for the construction of middle offices, including operation, maintenance, development, and management; Sm@rtGL, a transaction-grade general ledger system featuring concurrency, data volume, and requiring efficiency and flexibility; Sm@rtEMSP, an enterprise microservice platform that serves as a financial architecture software solution for the financial industry offering extensive and flexible reusable capabilities, concentrated/centralized management of basic platform components, and unified technical capability; Sm@rtEnsemble, a core banking system that serves as a banking business processing system; and Sm@rtTeller X, an integrated smart counter system used for human-machine interaction and business scenario capabilities. In addition, the company offers computer system integration; surveying and mapping; software, hardware, and technology development; network optimization; investment management; information technology; and technical services, as well as sells financial equipment. It serves financial institutions, regional institutions, banks, and fintech providers and partners. The company was formerly known as Digital China Information Service Company Ltd. and changed its name to Digital China Information Service Group Company Ltd. in August 2023. Digital China Information Service Group Company Ltd. is headquartered in Beijing, the People's Republic of China.

Graph Analytics in Banking Market Outlook

According to our latest research, the global market size for Graph Analytics in Banking reached USD 2.18 billion in 2024, with a robust year-on-year growth driven by the increasing adoption of advanced analytics solutions across the banking sector. The market is projected to expand at a CAGR of 22.1% from 2025 to 2033, reaching an estimated USD 15.07 billion by 2033. This remarkable growth is primarily fueled by the rising need for real-time fraud detection, enhanced risk management, and personalized customer experiences within the banking industry, as financial institutions globally are leveraging graph analytics to unlock deeper insights from complex data relationships.

The growth trajectory of the Graph Analytics in Banking market is underpinned by the increasing complexity and volume of financial transactions, which has made traditional analysis methods less effective. Banks are now facing more sophisticated fraud schemes, regulatory requirements, and customer demands for personalized services. Graph analytics enables the identification of hidden patterns, relationships, and anomalies in large and interconnected datasets, making it an indispensable tool for modern banking operations. The surge in digital banking, mobile payments, and online transactions has further amplified the need for advanced analytics to ensure security, compliance, and customer satisfaction, driving market adoption at an unprecedented pace.

Another significant growth factor is the integration of artificial intelligence (AI) and machine learning (ML) with graph analytics platforms. These technologies empower banks to automate the detection of suspicious activities, streamline compliance processes, and deliver targeted product recommendations. Graph analytics, when combined with AI/ML, enhances the accuracy and speed of decision-making, reducing operational risks and improving overall efficiency. Furthermore, the availability of scalable cloud-based solutions has democratized access to advanced analytics, enabling even small and medium-sized banks to leverage these capabilities without significant upfront investments in infrastructure.

The regulatory landscape is also playing a pivotal role in shaping the Graph Analytics in Banking market. Financial authorities worldwide are imposing stricter compliance requirements related to anti-money laundering (AML), know your customer (KYC), and fraud prevention. Graph analytics provides banks with the tools to efficiently map and monitor complex networks of transactions and relationships, ensuring adherence to regulatory standards. As regulatory scrutiny intensifies, the demand for robust analytics solutions that can provide transparency, traceability, and real-time monitoring is expected to continue rising, further propelling market expansion.

From a regional perspective, North America currently dominates the Graph Analytics in Banking market, accounting for the largest share in 2024, thanks to the presence of major financial institutions, advanced technological infrastructure, and early adoption of analytics solutions. Europe follows closely, driven by stringent regulatory frameworks and a strong focus on digital transformation within the banking sector. Meanwhile, the Asia Pacific region is emerging as the fastest-growing market, supported by rapid digitization, a burgeoning fintech ecosystem, and increasing investments in banking technology. These regional dynamics are expected to shape the competitive landscape and growth opportunities in the coming years.

Component Analysis

The Component segment of the Graph Analytics in Banking market is bifurcated into software and services. The software sub-segment encompasses platforms and tools that facilitate the visualization, analysis, and interpretation of complex data relationships within banking operations. These software solutions are designed to integrate seamlessly with existing banking systems, providing real-time insights into customer behaviors, transaction patterns, and potential risks. The rapid evolution of graph database technologies and analytics algorithms has significantly enhanced the capabilities of these software platforms, enabling banks to handle ever-increasing volumes of structured and unstructured data with ease.

On the other hand, the services sub-segment includes consulting, implementation, sup

The Kingdom of Saudi Arabia (KSA) Cash Management Services market, valued at $57.86 million in 2025, is projected to exhibit steady growth, driven by factors such as increasing digitalization, the expansion of the financial technology (Fintech) sector, and a growing need for efficient treasury management solutions amongst businesses of all sizes. The rising adoption of cloud-based solutions and mobile banking applications is further accelerating market expansion. Government initiatives promoting financial inclusion and digital transformation are also creating a favorable environment for market growth. However, challenges such as stringent regulatory compliance and the potential for cyber security threats pose restraints on market expansion. Competition within the market is robust, with both international and domestic players vying for market share. The market is segmented by service type (e.g., cash forecasting, payments processing, liquidity management), deployment mode (cloud-based, on-premise), and end-user industry (e.g., banking, finance, retail). Analysis of import and export data reveals a net positive trade balance, indicating a strong domestic demand alongside a degree of export-oriented activity. Price trends suggest a degree of stability, with minimal fluctuations expected over the forecast period. The forecast period (2025-2033) anticipates a continuation of this moderate growth trajectory, with a Compound Annual Growth Rate (CAGR) of 2.03%. This growth will be fueled by the ongoing digital transformation within the KSA financial sector, complemented by increasing government investment in infrastructure and technological upgrades. While competitive pressures will remain, strategic alliances and technological innovations will likely be key differentiators for market players, contributing to market consolidation over time. Further research focusing on the specific performance of each segment and the market impact of various regulatory changes will allow for a more precise prediction of future market growth. The diverse regional distribution across the KSA will likely require a tailored approach for market penetration by service providers. This in-depth report provides a comprehensive analysis of the Kingdom of Saudi Arabia (KSA) cash management services market, offering invaluable insights for businesses operating within or planning to enter this dynamic sector. Covering the period from 2019 to 2033, with a focus on 2025, this study delves into market size, growth drivers, challenges, and future trends, providing a robust foundation for strategic decision-making. The report utilizes a substantial data set, analyzing key market segments, including production, consumption, import/export, and price trends, to paint a complete picture of the KSA cash management landscape. The market is valued in millions of USD. Recent developments include: February 2023 - Bank AlJazira and American Express Saudi Arabia signed a new partnership contract to provide American Express Cardmembers access to more than 600 ATMs nationwide. The agreement assists American Express Saudi Arabia's guarantee towards increasing the number of sites in which its Cardmembers can contact its services. By permitting ATM withdrawal transactions access to be processed more suitability, American Express Saudi Arabia pursues to strengthen its presence within the country and expand value assistance to its Cardmembers., December 2022 - In partnership with Visa, Saudi British Bank launched the SABB VISA Cashback Platinum Credit Card. This credit card is available to all customers for free for life. The card offers various features and rewards, including offers, rewards, and cashback of up to 10% of the value of purchases automatically deposited into the customer's account each month., November 2022 - American Express Saudi Arabia and Marriott Bonvoy launched the Marriott Bonvoy American Express Credit Card, the first credit card that offers lodging rewards in Saudi Arabia. The new card issued by American Express Saudi Arabia allows cardmembers to earn Marriott Bonvoy points on their daily spending while giving them exclusive benefits, offers, and experiences across Marriott Bonvoy's portfolio.. Key drivers for this market are: 4., Growing Demand For Automation and Optimization of Working Capital Among Various Retailers4.; Increasing Adoption of Debit and Credit Cards4.; Adoption of AI and Advanced Analytics to Predict Patterns In Cash Forecasting. Potential restraints include: 4., Shift Toward Non-cash Transaction4.; Software Incompatibility During Expansions, Lack of Expertise, and Insufficient Cash Management Skills. Notable trends are: Growing Demand For Automation and Optimization of Working Capital Among Various Retailers is Expected to Drive the Market.