Alternative data – Enhancing accuracy in fraud detection

Traditional fraud detection methods are struggling to keep up with increasingly sophisticated tactics, creating a pressing need for more robust and adaptive systems. This is where alternative data emerges as a diverse reservoir of information capable of revolutionizing fraud detection strategies. By harnessing non-traditional sources, organizations can significantly enhance the accuracy, efficiency, and predictive power of their fraud detection mechanisms.

This article explores how alternative data can strengthen detection models, introduce new security paradigms, and transform risk management approaches. Whether you’re a data scientist, risk manager, or simply interested in the future of fraud detection, you’ll find valuable insights into this cutting-edge domain.

Table of Contents

What is alternative data?

Alternative data refers to information gathered from non-traditional sources, distinct from conventional financial and transactional data typically used in fraud detection and risk management. Unlike traditional data, which is structured and comes from well-known databases like credit scores and banking records, alternative data originates from diverse, unconventional sources. By incorporating this broader spectrum of data, organizations can significantly enhance the accuracy and predictive power of their fraud detection systems.

Main types of alternative data

Alternative data encompasses a variety of non-traditional sources that can significantly enhance fraud detection mechanisms when effectively utilized. While these data types differ in origin and structure, they share a common goal: to provide deeper insights into behaviours, activities, and trends that traditional data alone might miss. The main types of alternative data include:

Behavioural data

Behavioural data provides insights into how individuals interact with digital platforms and services. By analyzing user behaviours, organizations can detect patterns that deviate from the norm, indicating potential fraudulent activity.

Social media activity: Analyzing posts, comments, and interactions to assess risk profiles and detect fraudulent behaviours.
Clickstream data: Tracking users’ navigation patterns on websites to identify unusual or suspicious actions.
Social sentiment and product reviews: Monitoring sentiment analysis and user reviews to detect coordinated fraudulent activities or fake reviews.

Geospatial data

Geospatial data involves information about the physical location and movement of individuals. This data type helps verify user locations and detect anomalies that may suggest fraudulent activities.

Geolocation data: Adopting GPS data to verify user locations and detect anomalies in movement patterns.
Travel logs: Monitoring travel patterns to identify inconsistencies that could suggest fraudulent activity.
Satellite imagery: Employing satellite data to verify physical changes at locations, detect fraudulent claims, and assess large-scale activities.

Transactional data

Transactional data captures details of financial and commercial exchanges. By scrutinizing these transactions, organizations can identify irregularities that might indicate fraud.

E-commerce information: Assessing purchase histories across different online platforms to spot irregularities.
Point-of-sale data: Analyzing transactions at physical retail outlets for discrepancies.

Communication data

Communication data encompasses information exchanged through various communication channels. Analyzing this data can reveal patterns indicative of fraudulent activities.

Email Metadata: Examining the structure and flow of email communications to identify abnormal patterns.
Call Records: Analyzing call data for inconsistencies that may indicate fraudulent activity.

Sensor and IoT data

Sensor and IoT data come from connected devices and sensors embedded in various environments. This data type helps verify physical presence and activity consistency.

Smart device outputs: Gathering data from connected devices to verify physical presence and activity consistency.
Wearable technology: Using data from fitness trackers and other wearable devices to cross-verify identities.
Internet of Things (IoT): Analyzing data from a network of connected devices to monitor and identify irregular patterns.

Digital interaction data

Digital interaction data involves tracking user interactions with digital platforms and applications. This data type helps identify unusual activity that might indicate fraud.

Web traffic and app usage: Monitoring website and app usage data to detect unusual activity, such as sudden spikes in traffic or abnormal usage patterns.

Public and third-party data

Public and third-party data come from external sources, such as government records and third-party databases. This data type helps verify user-provided information and assess risk.

Public Records: Leveraging government and regulatory data, including property records and court filings.
Employment and education history: Using background data for identity verification and risk assessment.
Jet tracking: Tracking and verifying private jet movements to detect inconsistencies that could indicate fraudulent claims or activities.

Examples of alternative data

To gain a more practical understanding of how alternative data can be applied, consider these specific examples. These cases illustrate how various types of alternative data can be leveraged to enhance fraud detection efforts:

Social Media activity:

Example: A financial institution cross-references social media activity with known user profiles to detect impersonation attempts. Unusual behaviour, such as sudden changes in friends list or unusual posting patterns, could trigger further investigation.

Geolocation data:

Example: A banking app uses geolocation data to ensure that a transaction made from an overseas location aligns with the user’s recent travel patterns. If a user’s account is accessed from Tokyo immediately after being used in New York, it raises a red flag for potential fraud.

Clickstream data:

Example: An e-commerce site analyzes users’ clickstream data to understand typical browsing and purchasing patterns. A sudden shift to bulk purchasing or erratic browsing could indicate a compromised account.

Social sentiment and product reviews:

Example: An online marketplace employs sentiment analysis on product reviews to detect fake reviews. A sudden influx of overly positive or negative reviews could signal a fraudulent campaign.

Satellite imagery:

Example: An insurance company uses satellite imagery to verify claims about property damages. Discrepancies between reported damages and satellite images can raise flags for potential fraud.

Email metadata:

Example: A cybersecurity firm examines email metadata to identify unusual communication patterns, such as sudden spikes in outbound emails or connections to suspicious IP addresses, which may indicate a phishing attempt or account takeover.

Smart device outputs:

Example: A home security company uses data from smart cameras and motion sensors to validate the identity and presence of occupants. Discrepancies in expected versus actual device usage patterns can flag potential intrusions.

Web traffic and app usage:

Example: A mobile app developer monitors app usage data to spot abnormal patterns, such as sudden spikes in usage from a single IP address, indicating possible automated fraud.

Public records:

Example: A loan provider cross-references a loan applicant’s claims with public records, such as property ownership or court filings. Inconsistencies between provided information and public data can indicate fraudulent intentions.

Employment and education history:

Example: During the hiring process, a corporation verifies a candidate’s employment and education history through third-party data sources. Discrepancies in listed versus verified employment periods could signal possible resume fraud.

Jet tracking:

Example: A luxury service provider uses jet tracking data to cross-verify the movements of private jets with client itineraries. Any inconsistencies in reported versus actual movements could indicate fraudulent behaviour.

These examples highlight the versatility and potential of alternative data in providing a more comprehensive, multi-dimensional approach to fraud detection. By integrating such diverse data sources, organizations can more accurately identify and thwart fraudulent activities before they escalate.

How is alternative data collected and shared?

Alternative data is collected and shared using various methods and technologies designed to capture, process, and distribute non-traditional information. Understanding these methods is crucial for leveraging alternative data in fraud detection, risk management, investment decisions, and supply chain optimization.

Alternative data collection methods

APIs (Application Programming Interfaces): APIs allow access to data from various platforms and services. Social media APIs, for example, enable the collection of user interactions and posts, while other APIs gather real-time credit card transaction data.
Web scraping: Automated tools extract data from websites, converting raw data from public forums, review sites, and online marketplaces into a usable format for analysis.
Sensor networks: IoT devices like smart home systems and wearables continuously capture data on user activities, physical presence, and environmental conditions, enriching alternative data sets.
Geolocation services: GPS-enabled devices collect geolocation data to track movement patterns and verify locations, useful for validating supply chains and logistics.
Transactional data aggregation: Financial institutions and e-commerce platforms aggregate card transaction data and other transactional data to analyze purchase histories and detect fraudulent credit card transactions.
Public records databases: Public records, such as property records and court filings, are collected from official databases, enhancing the richness of alternative data sets.
Satellite and aerial imaging: High-resolution images from satellites and drones monitor physical changes at specific locations, useful for verifying insurance claims and evaluating investment decisions.
Communication logs: Email metadata and call records are collected through service providers and cybersecurity tools to identify abnormal patterns and potential security threats.

Alternative data sharing methods

Data marketplaces: Platforms where organizations buy and sell diverse alternative data types, facilitating investment decisions for hedge funds and financial institutions.
Data exchanges: Facilitate secure data sharing between organizations, ensuring compliance and data quality, particularly for sensitive data like credit card transactions.
Partnerships and collaborations: Organizations collaborate with businesses, research institutions, or data providers to enhance data richness for informed investment and supply chain management.
Cloud-based platforms: Scalable infrastructure for securely storing and sharing large data volumes with authorized partners in real-time, maintaining data usability.
Open data initiatives: Governments and institutions promote transparency by making public records freely available, enabling diverse alternative data usage.
APIs and data feeds: Enable continuous, real-time data sharing between systems to keep data current for fraud detection and decision-making.
Secure file transfers: Use secure protocols (SFTP) and encrypted email to protect privacy during sensitive data exchanges, such as credit card transactions.

Implementing these methods allows organizations to maximize alternative data benefits in fraud detection, investment decisions, and supply chain optimization, while ensuring data quality, compliance, and privacy are maintained.

Benefits of alternative data

Incorporating alternative data into fraud detection systems offers a range of advantages that can significantly enhance accuracy, efficiency, and overall effectiveness. Here are some key benefits:

Enhanced accuracy: Leveraging diverse data sources provides a more comprehensive view of user behaviour, enabling organizations to identify fraudulent patterns with greater precision.

Comprehensive analysis: Leveraging alternative data allows for more comprehensive analysis, enabling businesses to develop more effective strategies to combat fraud and adapt swiftly to the evolving tactics of cybercriminals.

Improved predictive power: Real-time insights from alternative data allow for quicker and more effective responses to potential threats, enhancing predictive capabilities.

Holistic risk assessment: The multi-dimensional nature of alternative data enables thorough evaluations of risk, covering behavioural nuances and real-time location data often missed by traditional methods.

Fraud pattern identification: Data from sources like social media activity and web traffic helps detect subtle indicators of coordinated fraud efforts, such as fake reviews or social engineering schemes.

Enhanced verification: Using data from geolocation, IoT, and public records aids in accurate user identity verification, reducing false positives and negatives.

Dynamic adaptation: Access to diverse data allows fraud detection systems to adapt more readily to new and evolving fraud tactics, keeping ahead of fraudsters.

Disadvantages of alternative data

Privacy concerns: Collecting and using alternative data raises significant privacy issues, necessitating compliance with stringent data protection laws and regulations.

Data quality issues: Unstructured and inconsistent data can compromise the accuracy and reliability of fraud detection efforts if not managed effectively.

Integration challenges: Combining alternative data with existing systems can be technically complex and resource-intensive, requiring specialized tools and expertise.

Cost considerations: Acquiring, storing, and processing vast amounts of alternative data can incur high costs, demanding careful cost-benefit analysis.

Operational complexity: Managing and analyzing diverse data sources increases operational complexity, necessitating robust data governance frameworks and skilled personnel.

Ethical concerns: Extensive use of alternative data may raise ethical concerns, including potential biases in automated decision-making processes.

False positives: While alternative data reduces false negatives by catching more fraud, it can also increase false positives, potentially flagging legitimate activities as fraudulent and inconveniencing customers.

By carefully evaluating these benefits and challenges, organizations can make informed decisions about integrating alternative data into their fraud detection strategies, ensuring they maximize advantages while mitigating potential downsides.

Why is alternative data important?

Alternative data is crucial in today’s fraud detection landscape due to its ability to provide a more comprehensive and nuanced understanding of user behaviour and activities. Traditional data sources often fall short in identifying sophisticated fraud tactics, but alternative data, such as social media activity, geolocation information, and transaction histories, fills these gaps.

By incorporating these diverse data sets, organizations can enhance the accuracy and predictive power of their fraud detection systems, allowing for quicker identification and response to potential threats. Additionally, alternative data supports more holistic risk assessments and adaptive strategies, ensuring that businesses stay one step ahead of evolving fraud schemes.

Who uses alternative data?

While this article primarily explores the use of alternative data in fraud detection across various industries, alternative data serves a broad spectrum of applications beyond this domain.

Alternative data is used by:

Investment firms: For predictive modeling in financial markets and assessing investment opportunities based on non-traditional indicators.
Retail and consumer goods: To analyze consumer behavior, forecast demand, and optimize pricing strategies using data from social media, IoT devices, and more.
Healthcare: For population health management, disease surveillance, and personalized medicine by leveraging data from wearable devices and patient records.
Transportation and logistics: To optimize routes, predict maintenance needs, and enhance supply chain efficiency using geospatial and IoT data.
Government and public sector: For urban planning, disaster response, and policy-making based on real-time data from sensors, satellites, and social media.

Alternative data in finance

In finance, alternative data enriches traditional financial metrics with unconventional insights. Financial institutions and investment firms rely on sources like social media sentiment, web traffic patterns, satellite imagery, and IoT data to inform investment strategies, assess market trends, and strengthen fraud detection. These sources offer real-time insights that complement traditional data, providing a more dynamic view of market conditions, consumer behaviors, and potential fraud.

By leveraging alternative data, finance professionals improve decision-making, optimize investments, and enhance real-time fraud detection, safeguarding assets and maintaining stakeholder trust.

Best practices for using alternative data

Effectively leveraging alternative data requires a balanced approach that integrates advanced technology with ethical considerations. Here are key best practices to ensure success:

Build a versatile tech stack

Creating a capable tech stack is foundational for maximizing alternative data:

Data integration platforms: Adopt solutions that seamlessly integrate diverse data sources, including social media data, credit histories, and other types of data, both structured and unstructured.
Advanced analytics tools: Implement sophisticated analytics capable of handling large datasets and performing real-time analysis, such as credit reporting and data analytics.
Machine learning algorithms: Employ AI and machine learning to uncover patterns and anomalies within alternative data.
Secure data storage: Invest in scalable, secure storage solutions to manage the increasing volume of alternative data.

Prioritize data security

Maintaining robust data security measures is essential to safeguard sensitive information:

Encryption: Employ strong encryption methods for data in transit and at rest to protect against unauthorized access.
Access control: Implement strict access controls to ensure only authorized personnel can access and manipulate data.
Regular audits: Conduct routine security audits to identify and address vulnerabilities in your systems.
Regulatory compliance: Adhere to data protection regulations such as GDPR and CCPA to protect consumer privacy and avoid legal penalties.

Foster accessibility and collaboration

Promote transparency and collaboration across departments and external partners:

Internal sharing: Ensure relevant teams like risk management, IT, and customer service have access to data insights.
External collaboration: Share findings with regulatory bodies or industry peers to enhance collective fraud prevention efforts.
Training and education: Provide ongoing training to stakeholders to enhance understanding and the use of alternative data.

Monitor and evaluate effectiveness

Continuously assess the impact of alternative data initiatives to ensure they meet objectives:

Performance metrics: Define KPIs to measure the effectiveness of alternative data in improving fraud detection outcomes.
Feedback loops: Establish mechanisms to gather feedback from stakeholders to refine data strategies continually.
Iterative improvement: Use assessment results to iterate and enhance data collection and analysis methods.
Benchmarking: Compare performance against industry standards to gauge effectiveness and identify areas for improvement.

Ensure data quality and accuracy

Maintain data integrity to maximize the reliability of your fraud detection efforts:

Data cleansing: Regularly cleanse and standardize data to eliminate inaccuracies and inconsistencies.
Source verification: Verify the credibility and reliability of data sources, including social media data and credit histories, to ensure accurate insights.
Timely updates: Keep data current and relevant, incorporating credit reporting and other relevant data, to support accurate decision-making and fraud detection.

Uphold ethical standards

Adhere to ethical guidelines to build trust and ensure responsible data practices:

Data minimization: Collect and retain only necessary data, such as credit reporting and other critical data types, to fulfill specific purposes.
Informed consent: Obtain consent from individuals for data collection and use, ensuring transparency in data handling practices.
Bias mitigation: Regularly audit data and algorithms for biases that could impact decision-making processes.

Cultivate continuous learning

Stay ahead in fraud detection through ongoing education and innovation:

Training initiatives: Update training programs to include the latest advancements in fraud detection technologies.
Industry engagement: Participate in industry events and forums to stay informed about emerging trends and best practices.
Research and development: Invest in R&D to explore new ways to leverage alternative data, including social media data and credit histories, for enhanced fraud detection capabilities.

By following these best practices, organizations can effectively harness the power of alternative data to strengthen fraud detection capabilities, maintain data security, and foster ethical and sustainable practices in data management.

Alternative data use by aiReflex

aiReflex by fraud.com leverages alternative data to enhance fraud detection capabilities across various sectors. By integrating unconventional data sources such as social media activity, geolocation data, transaction histories, and metadata, aiReflex enhances the accuracy, efficiency, and predictive power of fraud detection mechanisms. This approach not only fortifies existing detection models but also introduces new paradigms of security, ensuring robust protection against sophisticated fraud tactics in industries ranging from finance and e-commerce to healthcare and beyond.

Alternative data FAQ

Question	Answer
What is alternative data?	Alternative data refers to non-traditional data sources, such as social media activity, geolocation data, and IoT device outputs.
How is alternative data used in fraud detection?	Alternative data enhances fraud detection by providing real-time insights and diverse behavioral patterns beyond traditional financial data.
What are examples of alternative data sources?	Examples include social media sentiment, web traffic patterns, satellite imagery, IoT device data, and public records.
How can organizations access alternative data?	Access methods include data marketplaces, exchanges, partnerships, cloud platforms, APIs, and secure file transfers.
What are the benefits of using alternative data?	Benefits include enhanced accuracy in predictions, improved risk assessment, and real-time monitoring capabilities in various industries.
What are the challenges of using alternative data?	Challenges include data privacy concerns, data quality issues, integration complexity, and potential biases in data analysis.
How can alternative data be integrated into existing systems?	Integration involves using advanced analytics tools, APIs for data feeds, and ensuring compliance with data protection regulations.
What industries benefit most from alternative data?	Finance, healthcare, retail, logistics, and government sectors leverage alternative data for fraud detection, market analysis, and more.
What ethical considerations should be addressed when using alternative data?	Ethical concerns include data privacy, bias mitigation, and transparency in data usage and decision-making processes.