This dataset provides a detailed look into transactional behavior and financial activity patterns, ideal for exploring fraud detection and anomaly identification. It contains 2,512 samples of transaction data, covering various transaction attributes, customer demographics, and usage patterns. Each entry offers comprehensive insights into transaction behavior, enabling analysis for financial security and fraud detection applications.

Key Features:

• TransactionID: Unique alphanumeric identifier for each transaction.
• AccountID: Unique identifier for each account, with multiple transactions per account.
• TransactionAmount: Monetary value of each transaction, ranging from small everyday expenses to larger purchases.
• TransactionDate: Timestamp of each transaction, capturing date and time.
• TransactionType: Categorical field indicating 'Credit' or 'Debit' transactions.
• Location: Geographic location of the transaction, represented by U.S. city names.
• DeviceID: Alphanumeric identifier for devices used to perform the transaction.
• IP Address: IPv4 address associated with the transaction, with occasional changes for some accounts.
• MerchantID: Unique identifier for merchants, showing preferred and outlier merchants for each account.
• AccountBalance: Balance in the account post-transaction, with logical correlations based on transaction type and amount.
• PreviousTransactionDate: Timestamp of the last transaction for the account, aiding in calculating transaction frequency.
• Channel: Channel through which the transaction was performed (e.g., Online, ATM, Branch).
• CustomerAge: Age of the account holder, with logical groupings based on occupation.
• CustomerOccupation: Occupation of the account holder (e.g., Doctor, Engineer, Student, Retired), reflecting income patterns.
• TransactionDuration: Duration of the transaction in seconds, varying by transaction type.
• LoginAttempts: Number of login attempts before the transaction, with higher values indicating potential anomalies.

This dataset is ideal for data scientists, financial analysts, and researchers looking to analyze transactional patterns, detect fraud, and build predictive models for financial security applications. The dataset was designed for machine learning and pattern analysis tasks and is not intended as a primary data source for academic publications.

Transaction Banking Market Outlook

The global transaction banking market size was valued at approximately $1.2 trillion in 2023 and is expected to reach around $2.1 trillion by 2032, growing at a compound annual growth rate (CAGR) of 6.3%. The primary growth driver for this market includes the increasing demand for secure, efficient, and flexible banking solutions that cater to the needs of businesses and individuals in a fast-paced digital economy.

A significant factor contributing to the growth of the transaction banking market is the increasing global trade volumes. As businesses expand their operations beyond domestic borders, the need for sophisticated banking solutions to manage complex financial transactions, mitigate risks, and optimize cash flows becomes crucial. The rise of multinational corporations and the integration of global supply chains necessitates advanced transaction banking services, fostering the market's expansion. Furthermore, the adoption of innovative technologies such as blockchain and artificial intelligence in transaction banking solutions has enhanced security, transparency, and efficiency, thereby driving market growth.

Another pivotal growth driver is the rising trend of digital transformation across various industries. As businesses and consumers increasingly embrace digital banking channels, transaction banks are compelled to innovate and offer digital solutions that provide seamless and convenient banking experiences. The proliferation of smartphones and internet connectivity has further accelerated the shift towards digital transaction banking, making it essential for banks to invest in robust digital infrastructure and services. Additionally, regulatory initiatives promoting digital payments and financial inclusion in developing economies are expected to bolster the market's growth.

The increasing focus on enhancing customer experience and improving operational efficiencies also plays a significant role in driving the transaction banking market. Banks are increasingly leveraging data analytics and machine learning to gain insights into customer behavior and preferences, enabling them to offer personalized banking solutions and services. By optimizing backend processes through automation and advanced technologies, banks can reduce operational costs, minimize errors, and enhance overall efficiency, thereby gaining a competitive edge in the market. Furthermore, strategic partnerships and collaborations among banks, fintech companies, and technology providers are fostering innovation and expanding the range of transaction banking services offered.

Retail Banking Service plays a crucial role in the transaction banking market by providing essential financial services to individual consumers and small businesses. As the demand for personalized and convenient banking experiences grows, retail banking services are evolving to offer a wide range of digital solutions. These services include mobile banking, electronic funds transfer, and payment solutions that cater to the diverse needs of retail customers. The integration of advanced technologies such as artificial intelligence and data analytics in retail banking is enhancing customer experience by offering tailored financial products and services. Furthermore, the focus on financial inclusion and the proliferation of digital payment solutions are driving the growth of retail banking services, making them a vital component of the transaction banking ecosystem.

Regionally, the Asia Pacific region is expected to witness significant growth in the transaction banking market due to the rapid economic development, increasing trade activities, and the growing adoption of digital banking solutions. Countries such as China, India, and Southeast Asian nations are at the forefront of this growth, driven by favorable government policies, a large unbanked population, and the proliferation of mobile banking. North America and Europe also hold substantial market shares, owing to the presence of established banking institutions, a high level of digital literacy, and advanced financial infrastructure. Meanwhile, regions such as Latin America and the Middle East & Africa are emerging as potential markets, supported by improving economic conditions and increasing foreign investments.

Product Type Analysis

In the transaction banking market, the segment by product type includes Cash Management, Trade Finance, Payments and Collections, and Others. Cash Management servi

Envestnet®| Yodlee®'s Bank Statement Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

This dataset provides a detailed, feature-rich record of synthetic banking transactions, including transaction metadata, account and merchant information, contextual behavioral features, and fraud labels. It is ideal for developing, training, and benchmarking machine learning models for fraud detection and anomaly analysis in financial services.

Uk account level user banking data set including bank account transactions (Traditional and Neo Banks like Revolut/Monzo etc.), card spending across credit and debit cards and ticker.

All ongoing transaction data is delivered in real time which is a significant advantage over stale data other providers offer.

Whether you’re looking for trends in retail data to inform investment decisions or to understand consumer behavior over a larger set of consumers, Real Time UK Consumer Transaction Data, de-identified gives you a distinct advantage because of our real time view into the customer at the account level (Not just debit or credit cards).

• Access the “truth” of transaction data across all of a users accounts (Traditional banks, Neo Banks, Credit and Debit cards) • Insight into Millennial, Gen Z, and the Underbanked segments that are missing in other transaction data sets • Analyze transactions with greater enriched detail • Protect consumer privacy and comply with local regulation while enabling more flexible analysis of safe data

Dataset

This dataset was created by bhadresh savani

Contents

ExactOne delivers unparalleled consumer transaction insights to help investors and corporate clients uncover market opportunities, analyze trends, and drive better decisions.

Dataset Highlights - Source: Debit and credit card transactions from 600K+ active users and 2M accounts connected via Open Banking. Scale: Covers 250M+ annual transactions, mapped to 1,800+ merchants and 330+ tickers. Historical Depth: Over 6 years of transaction data. Flexibility: Analyse transactions by merchant/ticker, category/industry, or timeframe (daily, weekly, monthly, or quarterly).

ExactOne data offers visibility into key consumer industries, including: Airlines - Regional / Budget Airlines - Cargo Airlines - Full Service Autos - OEMs Communication Services - Cable & Satellite Communication Services - Integrated Telecommunications Communication Services - Wireless Telecom Consumer - Services Consumer - Health & Fitness Consumer Staples - Household Supplies Energy - Utilities Energy - Integrated Oil & Gas Financial Services - Insurance Grocers - Traditional Hotels - C-corp Industrial - Misc Industrial - Tools And Hardware Internet - E-commerce Internet - B2B Services Internet - Ride Hailing & Delivery Leisure - Online Gambling Media - Digital Subscription Real Estate - Brokerage Restaurants - Quick Service Restaurants - Fast Casual Restaurants - Pubs Restaurants - Specialty Retail - Softlines Retail - Mass Merchants Retail - European Luxury Retail - Specialty Retail - Sports & Athletics Retail - Footwear Retail - Dept Stores Retail - Luxury Retail - Convenience Stores Retail - Hardlines Technology - Enterprise Software Technology - Electronics & Appliances Technology - Computer Hardware Utilities - Water Utilities

Use Cases

For Private Equity & Venture Capital Firms: - Deal Sourcing: Identify high-growth opportunities. - Due Diligence: Leverage transaction data to evaluate investment potential. - Portfolio Monitoring: Track performance post-investment with real-time data.

For Consumer Insights & Strategy Teams: - Market Dynamics: Compare sales trends, average transaction size, and customer loyalty. - Competitive Analysis: Benchmark market share and identify emerging competitors. - E-commerce vs. Brick & Mortar Trends: Assess channel performance and strategic opportunities. - Demographic & Geographic Insights: Uncover growth drivers by demo and geo segments.

For Investor Relations Teams: - Shareholder Insights: Monitor brand performance relative to competitors. - Real-Time Intelligence: Analyse sales and market dynamics for public and private companies. - M&A Opportunities: Evaluate market share and growth potential for strategic investments.

Key Benefits of ExactOne - Understand Market Share: Benchmark against competitors and uncover emerging players. - Analyse Customer Loyalty: Evaluate repeat purchase behavior and retention rates. - Track Growth Trends: Identify key drivers of sales by geography, demographic, and channel. - Granular Insights: Drill into transaction-level data or aggregated summaries for in-depth analysis.

With ExactOne, investors and corporate leaders gain actionable, real-time insights into consumer behaviour and market dynamics, enabling smarter decisions and sustained growth.

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]

Market Analysis of Bank Transaction Processing Systems The global bank transaction processing systems market size was valued at USD XX million in 2025 and is expected to expand at a CAGR of XX% from 2025 to 2033. The market is driven by increasing adoption of digital banking solutions, rising volume of online transactions, and growing need for efficient and secure transaction processing. Cloud-based solutions are gaining popularity due to their scalability, cost-effectiveness, and ease of implementation. Large enterprises and SMEs are major end-users of these systems. North America and Asia Pacific are key regional markets, with countries such as the United States, China, and India contributing significantly to the growth. Key Trends and Restraints The market is influenced by advancements in financial technologies, such as artificial intelligence and blockchain, which enable faster and more secure transactions. The adoption of mobile banking and e-commerce is fueling the growth of online transactions. However, concerns about data security and privacy, as well as the cost of implementing and maintaining these systems, remain as potential restraints. Regulatory compliance requirements and geopolitical instability also impact the market's trajectory. Leading companies in the market include Risco Software Sp, Due, Stripe, PayPal, and Square, among others. They continuously invest in innovation and partnerships to strengthen their offerings and gain market share.

This dataset provides detailed, interconnected banking transaction records, capturing sender and receiver relationships, transaction metadata, and anomaly flags. Designed for network analytics, it enables advanced anti-money laundering (AML) detection, fraud analysis, and financial behavior modeling by representing transactions as a directed graph. The flat structure ensures easy integration with machine learning and graph analytics tools.

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]

Bank Customer Segmentation

Most banks have a large customer base - with different characteristics in terms of age, income, values, lifestyle, and more. Customer segmentation is the process of dividing a customer dataset into specific groups based on shared traits.

According to a report from Ernst & Young, “A more granular understanding of consumers is no longer a nice-to-have item, but a strategic and competitive imperative for banking providers. Customer understanding should be a living, breathing part of everyday business, with insights underpinning the full range of banking operations.

About this Dataset

This dataset consists of 1 Million+ transaction by over 800K customers for a bank in India. The data contains information such as - customer age (DOB), location, gender, account balance at the time of the transaction, transaction details, transaction amount, etc.

Interesting Analysis Ideas

The dataset can be used for different analysis, example -

1. Perform Clustering / Segmentation on the dataset and identify popular customer groups along with their definitions/rules
2. Perform Location-wise analysis to identify regional trends in India
3. Perform transaction-related analysis to identify interesting trends that can be used by a bank to improve / optimi their user experiences
4. Customer Recency, Frequency, Monetary analysis
5. Network analysis or Graph analysis of customer data.

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

Description of the Dataset: A Comprehensive Record of Personal Savings Account Transactions

This dataset provides a detailed record of an individual's savings bank account transactions spanning the years 2022 and 2023. It encompasses a total of 10 columns, consisting of 6 primary columns and 4 secondary columns.

The 6 Primary Columns Are: 1. Date: This column records the date of each transaction. 2. Debit or Credit: It signifies whether each transaction involves a debit (money withdrawn) or a credit (money deposited). 3. Amount: This column documents the monetary value associated with each transaction. 4. Balance: It records the account balance after each transaction. 5. Method of Transaction: This column specifies the method or channel through which the transaction was executed. 6. Name of Person: It identifies the person involved in the transaction.

In addition to these primary columns, there is an essential secondary column: i.e., Tday (Transaction Day): This column is designed to highlight specific days with multiple transactions. It indicates the occurrence of multiple transactions on the same date, providing valuable information about account activity.

This dataset comprises a total of 509 transactions, and within these transactions, there are 313 unique transaction dates. This unique date count underscores that the dataset includes days with multiple transactions, and the 'Tday' column is utilized to distinguish and track such instances.

Refer the data for more insights. You will also be able to see Pareto distribution (Find it out!!)

Graph and download economic data for Use of Financial Services, Mobile Banking: Number of Mobile Money Transactions (during the Reference Year) for Bangladesh (BGDFCMTNUM) from 2011 to 2024 about Bangladesh, financial, services, banks, and depository institutions.

Turkey Internet Banking: Financial Transactions (FT): Value data was reported at 955,666.208 TRY mn in Mar 2018. This records a decrease from the previous number of 957,452.151 TRY mn for Dec 2017. Turkey Internet Banking: Financial Transactions (FT): Value data is updated quarterly, averaging 310,124.586 TRY mn from Mar 2007 (Median) to Mar 2018, with 45 observations. The data reached an all-time high of 957,452.151 TRY mn in Dec 2017 and a record low of 101,558.822 TRY mn in Mar 2007. Turkey Internet Banking: Financial Transactions (FT): Value data remains active status in CEIC and is reported by The Banks Association of Turkey. The data is categorized under Global Database’s Turkey – Table TR.KA010: Internet Banking Statistics.

Mobile Banking Transactions: Volume: Bank Of India data was reported at 19.137 Unit mn in Sep 2018. This records an increase from the previous number of 16.166 Unit mn for Aug 2018. Mobile Banking Transactions: Volume: Bank Of India data is updated monthly, averaging 0.001 Unit mn from May 2009 (Median) to Sep 2018, with 113 observations. The data reached an all-time high of 19.137 Unit mn in Sep 2018 and a record low of 0.000 Unit mn in Apr 2010. Mobile Banking Transactions: Volume: Bank Of India 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.KAI020: Mobile Banking Transactions: by Bankwise.

The global transactional banking market is projected to reach USD 1,896.6 million by 2033, at a CAGR of 9.5% from 2025 to 2033. The increasing adoption of digital technologies, growing demand for trade finance solutions, and stringent regulatory requirements are key factors driving market growth. The need for efficient and secure financial transactions, coupled with the growing complexity of global supply chains, is also contributing to the market's expansion. Asia-Pacific is expected to hold a dominant position in the transactional banking market throughout the forecast period. The region's rapid economic growth, expanding trade volumes, and increasing adoption of fintech solutions are propelling market growth. North America and Europe are also significant markets, with advanced financial infrastructures and a high demand for trade finance services. However, emerging economies in the Middle East and Africa are expected to witness significant growth in the coming years due to increasing foreign investments and trade activities.

Explore the bank deposits data distributed by type in Saudi Arabia, including demand deposits, time & savings accounts, repo transactions, and more. Gain valuable insights into the country's financial landscape with this comprehensive dataset.

Demand Deposits : Business & Individuals, Demand Deposits : Total, In Foreign Currency : Government Entities, Time & Savings : Business & Individuals, Repo Transactions, Total Deposits, Letters of Credit, Time & Savings : Total, Other Quasi-Money, total_quasi_monetary_deposits, Outstanding Remittances, Demand Deposits : Government Entities, In Foreign Currency : Business & Individuals, Time & Savings : Government Entities, In Foreign Currency : Total, Business, Currency, Transactions, Money, Bank, SAMA Monthly

Saudi ArabiaFollow data.kapsarc.org for timely data to advance energy economics research.. Important notes:Other Quasi-Money: comprise residents' foreign currency deposits, marginal deposits for LCs, outstanding remittances, and banks Repo transactions with private sector.The data are updated. The data of foreign bank branches operating in Saudi Arabia have been amended and updated as per international best practices and the Monetary and Financial Statistics Manual.

This dataset provides detailed, structured records of bank transactions, each annotated with a synthetic fraud risk score and binary fraud label. It includes transaction, account, merchant, and contextual information, making it ideal for training and evaluating fraud detection and risk assessment models. The dataset supports feature engineering for advanced analytics and machine learning applications in financial security.