The Complete Guide to Alternative Data: Types, Benefits, and Challenges

What is Alternative Data?

What is alternative data? In simple terms, it refers to any data not traditionally collected or used in standard models. This can include digital behaviour, app usage patterns, satellite images, or social sentiment.

‍

The definition of alternative data varies by industry, but it always points to unconventional data points that provide added value. 

‍

In finance, it helps reveal insights into consumer behaviour, market signals, and risk profiles that are invisible through traditional channels.

‍

Defining Alternative Data vs. Traditional Data

Traditional data typically refers to structured information sourced from banks, governments, and credit bureaus—such as credit scores, income records, or employment history. 

‍

It is usually static, updated periodically, and housed in centralised systems. 

‍

Meanwhile, alternative data sets originate from decentralised, digital-first sources like mobile apps, smart devices, websites, IoT systems, and social media platforms. 

‍

These signals tend to be unstructured or semi-structured and are often captured in real time or at high frequency.

‍

Alternative data is highly valuable for use cases like behavioural scoring and fraud detection, whereas traditional data is more commonly used for loan approval and Know Your Customer (KYC) processes.

‍

Ultimately, alternative data vs. traditional data is not a question of one replacing the other. Rather, they complement each other. 

‍

When used together, they provide a more comprehensive and accurate understanding of individual users and broader market behaviour. For more insights, read more in How Alternative and Traditional Data Work Better Together.

‍

Why is Alternative Data Gaining Prominence?

Three powerful forces drive the shift toward alternative data:

‍

1. Digital transformation – As more people interact online, digital footprints become a valuable asset for understanding consumer habits.
2. Financial inclusion – Traditional credit scoring overlooks millions who lack formal records. Alternative data helps bring the invisible credit into the system.
3. Competitive advantage – From hedge funds to fintech lenders, companies are using alternative data for investors and scoring to act faster and more accurately.

‍

The Primary Types of Alternative Data with Examples

Some widely used alternative data examples include app usage behaviour, mobile wallet transactions, geolocation signals, psychometric surveys, and device metadata.

There is a wide variety of alternative data sources used across sectors. The types of alternative data can be categorised into distinct types:

‍

1. Web & App Data: Web Traffic, Scraping, and App Usage

Web and app usage data reveal critical insights into consumer habits. Information such as browsing patterns, app installation or uninstallation history, and time spent on specific platforms provides a real-time view of digital engagement. These behaviours are particularly valuable for alternative data analysis, as they can predict customer intent and highlight levels of financial reliability.

‍

2. Consumer Transaction Data: Credit Cards and E-receipts

Transaction-level data from credit cards, e-wallets, and electronic receipts paints a detailed picture of an individual’s financial health. By tracking spending habits, recurring payments, and cash flow discipline, lenders can evaluate repayment capacity more accurately. This type of data is especially useful in underwriting and in creating personalised credit products for different borrower segments.

‍

3. Geolocation & Satellite Data: Foot Traffic, Satellite Imagery, and Logistics

Location-based data provides actionable intelligence that goes beyond traditional credit metrics. Real-time foot traffic around retail outlets, for instance, can reflect consumer activity and potential business performance. Similarly, satellite imagery and logistics tracking data can be leveraged to optimise inventory, detect risks in supply chains, and gauge the stability of borrowers involved in commerce or transport.

‍

4. Social & Sentiment Data: Social Media Trends and Product Reviews

The way consumers engage online—whether through product reviews, brand mentions, or social media activity—offers a clear indicator of sentiment. Public opinion can directly influence company performance and individual credibility in financial contexts. For lenders, monitoring sentiment shifts provides an additional lens to assess risk, particularly within alternative data finance applications.

‍

5. Other Emerging Sources: IoT, Private Jet Tracking, and Weather Data

New technologies are constantly adding unconventional data streams that can be used for financial insights. Wearables and home assistants, for example, provide behaviour-based signals around activity and lifestyle. In more niche markets, data such as private jet tracking or weather conditions can help identify wealth patterns, travel frequency, and even regional credit risk factors.

‍

6. Credit Scoring Based on Digital Behaviour

Modern credit scoring based on digital behaviour uses app activity, device metadata, and online interaction patterns to evaluate creditworthiness. This method allows lenders to approve applicants quickly while remaining compliant with privacy standards. It is particularly effective for individuals without extensive financial histories, offering a fairer and more inclusive way to expand access to credit.

Key Benefits of Using Alternative Data

As finance becomes more digital, alternative data is becoming indispensable for smarter, faster, and more inclusive decision-making. By analysing behavioural patterns, digital signals, and real-time activity, lenders and businesses can gain insights that traditional credit sources cannot provide.

‍

Gaining a Competitive Edge in Financial Markets

Firms using alternative data for credit scoring detect subtle behavioural trends and digital signals often missed by legacy systems. This not only accelerates decision-making but also sharpens customer segmentation. The ability to make faster, more informed choices provides a significant competitive edge in crowded financial markets.

‍

Improving Financial Inclusion for the 'Thin-File' Population

Millions of consumers worldwide lack formal credit histories, making them invisible to traditional scoring systems. Alternative data sources such as app usage, payment behaviour, and smartphone activity, make accurate assessments possible. This inclusion allows underserved groups like students, freelancers, and first-time borrowers to gain fair access to credit.

‍

Enhancing Strategy and Market Research

Beyond credit scoring, companies are using alternative datasets to refine strategy and market intelligence. By benchmarking performance against digital behaviour trends, firms can identify market gaps and adjust planning. Mobile metadata and behavioural signals also help in product design, ensuring offerings align with consumer needs.

‍

Strengthening Risk Management and Fraud Detection

Fraud detection benefits significantly from alternative data. For example, Credolab’s Fraud SDK monitors device integrity and flags unusual behavioural patterns in real time. Detecting manipulation or abnormal usage early reduces fraudulent applications and strengthens overall portfolio resilience.

‍

Real-Time Insights

Unlike static bureau data, alternative data such as geolocation, app usage, and transaction activity provides dynamic and real-time signals. This immediacy allows lenders to respond quickly to risk changes with confidence. Rapid insights improve both decision quality and borrower experience.

‍

Improved Predictive Power

When combined with traditional bureau data, alternative inputs boost predictive accuracy across models. In credit risk analytics, this can generate a +11 Gini uplift and lower default rates by up to 30%. The result is stronger performance in lending portfolios and better long-term stability.

‍

Broader Data Coverage

Alternative data extends credit scoring models to freelancers, gig workers, and immigrants often excluded from traditional systems. By analysing e-wallet transactions, mobile payments, and utility bills, lenders gain meaningful insights into repayment ability. This broader coverage ensures credit access expands without compromising accuracy.

‍

Use of Unstructured Data and Alternative Data Types

Unstructured signals such as social sentiment, browsing patterns, or reviews represent powerful alternative data types. Machine learning converts these inputs into structured, actionable features that can be used for scoring, segmentation, and forecasting. By leveraging them effectively, lenders unlock deeper insights beyond traditional variables.

‍

Personalised Decision-Making

Digital behaviour provides a foundation for hyper-personalised financial services. By tailoring offers, pricing, and product recommendations to an individual’s data profile, companies increase engagement and trust. Over time, this leads to improved conversions, stronger customer loyalty, and long-term growth.

‍

Diversified Risk Analysis

Incorporating multiple streams of behavioural and transactional signals creates more resilient credit and fraud models. This layered approach reduces reliance on a single indicator and improves detection of anomalies. As a result, lenders achieve stronger portfolio health and reduced exposure to systemic risk.

‍

The Biggest Challenges and Risks of Alternative Data

While alternative data sources offer considerable benefits, they also bring unique challenges that require thoughtful management. These challenges shouldn’t discourage adoption; instead, they highlight the importance of strong governance, validation processes, and adherence to recognised frameworks such as Oliver Wyman’s data quality model.

‍

Data Security, Accuracy, and Fraud

Incomplete or unverified alternative datasets can undermine model reliability and create vulnerabilities. Without strong validation, fraudulent or manipulated data may distort outcomes and compromise fraud prevention models. Weak security protocols further heighten the risk of data misuse or breaches, making robust safeguards essential.

‍

Data Privacy and Ownership Concerns

Because alternative data in finance often comes from personal devices, apps, or digital platforms, questions of privacy and ownership naturally arise. Collecting this information without explicit consent risks breaching laws like GDPR, CCPA, or Brazil’s LGPD. Clear disclosure and privacy-consented collection are non-negotiable to protect users and institutions alike.

‍

Lack of Transparency and "Black Box" Models

Some ML models processing alternative data are difficult to interpret, raising concerns around explainability. This lack of transparency can erode trust, particularly in regulated use cases such as lending or insurance. Embedding explainable AI/ML (XAI) tools ensures both consumer confidence and compliance.

‍

Potential for Informed Gaming of the System

As awareness of tracked behaviours grows, applicants may attempt to manipulate their digital activity to appear more creditworthy. This risk highlights the importance of cross-checking multiple datasets and applying fraud detection tools that can spot abnormal or artificial behaviour patterns.

‍

Risk of Discriminatory Scoring and Bias

Biases in training data can result in unintended discrimination against vulnerable groups such as women, minorities, gig economy workers, or rural communities. Strong debiasing methods and fairness audits are vital to ensure that alternative data promotes financial inclusion rather than reinforcing inequalities.

‍

Unintended Consequences and Regulatory Hurdles

Regulations around data use vary widely across markets, creating uncertainty for lenders operating internationally. Weak or non-compliant models can trigger regulatory scrutiny and reputational damage. Proactive compliance measures and jurisdiction-specific data handling policies are crucial to mitigate this risk.

‍

Quality Control Issues

The diversity of alternative data types often leads to inconsistencies, noise, or duplicates. Oliver Wyman’s framework stresses the need for accuracy and timeliness: data must be validated, frequently updated, and subjected to robust governance to retain its predictive value.

‍

Complexity of Analysis

Extracting value from behavioural and device metadata requires advanced expertise. Organisations must invest in feature engineering, contextual modelling, and skilled ML teams to translate raw signals into actionable insights. Leveraging partners with proven experience can help bridge this capability gap.

‍

High Costs for Premium Datasets

Some alternative datasets—such as satellite imagery or granular transaction feeds—carry high costs. Smaller firms may struggle with affordability, potentially widening the gap between data-rich institutions and those with limited resources. Creative partnerships and scalable data sourcing strategies can help reduce this imbalance.

‍

Legal Uncertainty

Cross-border usage, residency rules, and consent requirements create legal complexity for alternative data adoption. Oliver Wyman’s framework highlights regulatory compliance as a key quality marker, underscoring the need for solutions that are designed with compliance-by-design principles.

‍

Integration with Existing Systems

Legacy infrastructures often lack the flexibility to integrate real-time or unstructured data. Aligning with the orthogonality principle in Oliver Wyman’s framework, firms must ensure that alternative datasets complement rather than conflict with existing systems, creating richer insights instead of operational friction.

‍

Limited Historical Depth

Many alternative data sources are relatively new and lack the long-term records needed for robust back-testing. This affects trend analysis and benchmarking. Oliver Wyman recommends assessing coverage and specificity to determine whether a dataset has sufficient breadth and detail to support reliable modelling.

‍

The Essential Role of AI and Machine Learning

While artificial intelligence (AI) draws attention, it’s ML that drives the real value of alternative data. ML models are essential for turning large volumes of unstructured digital signals into actionable insights.

‍

The correct use of alternative data through machine learning gives businesses a strategic edge, helping uncover behavioural patterns in both current and future customers. 

‍

With billions of behavioural signals generated daily, only ML can handle and extract value from complex alternative data sets.

‍

Used in fraud detection, credit scoring, and pricing, ML improves accuracy as more data flows in. 

‍

At Credolab, on-device ML analyses anonymised metadata to generate privacy-consented risk scores—ensuring compliance, security, and real-time performance across financial and digital platforms.

‍

Future Trends in Alternative Data

The future of alternative data in finance is shaped by enhanced privacy, smarter ML, and growing demand for transparency. 

‍

On-device processing and graph-based ML now allow real-time scoring and fraud detection without compromising data security.

‍

As alternative credit scoring models expand globally, mobile and telecom data are enabling access for underserved populations. 

‍

Meanwhile, regulatory expectations for explainable ML models are rising—pushing providers like Credolab to offer tools that make credit risk analytics more transparent and actionable.

‍

FAQs

What is alternative data in finance?

Alternative data in finance refers to non-traditional information used to assess creditworthiness, investment potential, or fraud risk. This includes digital behaviour, social sentiment, and mobile device data.

‍

Is using alternative data legal?

Yes—if collected and processed ethically. Consent is crucial. Reputable providers comply with laws like the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and Lei Geral de Proteção de Dados (LGPD).

‍

Where can I find alternative data sources?

Data can be collected through direct user interactions, like in applications or forms, third-party vendors, or built internally using digital platforms.

‍

Is alternative data expensive?

It depends. Some alternative data sources, like device metadata, are low-cost and high-yield. Others, like satellite data, may be more expensive to source and process.

‍

What is the difference between traditional data and alternative data?

Traditional data is often static and structured (e.g., bank statements), while alternative data includes dynamic, behavioural inputs, such as web activity and geolocation.

‍

Who uses alternative data?

Investors, banks, fintech companies, insurers, and marketing agencies all use alternative data for investors, credit decisions, fraud prevention, and customer targeting.

‍

What are the different types of alternatives?

Common alternative data types include app data, transaction logs, geolocation, social sentiment, and IoT signals.

‍

How big is the alternative data market?

The alternative data market is expected to reach over $17 billion by 2027, with rapid growth in adoption across developed and emerging markets.