April 29, 2026
Risk
July 28, 2022
Alternative Data
Top 10 Lending Tools Lenders Actually Use — Credit Scoring, Fraud Detection & More
In a fast-moving digital world, lenders need to manage risk, fraud and marketing quickly and efficiently. To do this, they must provide their teams with the right lending tools to speed up tasks and maximise all their working potential.
Alternative data solutions are a clear example of how new, effective lending tools can help lenders boost their businesses.
According to Mike Mondelli, former Senior Vice President of TransUnion Alternative Data Services, alternative data sources accurately score more than 90% of applicants, which traditional lenders would otherwise recognise as thin-files or credit invisibles.
In this article, we will review the 10+ main tools that facilitate the daily work of lenders, providing increased business control and increasing their profits at a lower risk. These effective lending tools are shaping the future of credit, fraud prevention, and marketing.
What Are Lending Tools?
Lending tools are software and data solutions that support credit analysis, fraud checks, pricing, and lifecycle marketing. They increasingly use machine learning (ML) and device and behavioural metadata to drive predictive decisions.
Credolab applies ML to anonymised, privacy-consented smartphone and web metadata. With ML, lenders can expect faster underwriting, richer risk signals, and more in-depth orthogonal insights that complement traditional bureau data.
Well-designed tools let you move beyond traditional scoring while staying compliant and auditable. Using the right credit lending tool helps lenders improve decision-making, reduce losses, and enhance customer satisfaction.
Why Lending Technology Matters in 2026
- Faster Decisions
Speed has become a major competitive factor in lending. Borrowers now expect quick approvals, clear communication, and fewer repeated document requests. Lending technology helps lenders meet these expectations by automating application intake, identity checks, income verification, data extraction, and pre-approval workflows. This reduces the time teams spend on manual review and helps applications move through the pipeline faster.
More importantly, faster decisions do not have to mean weaker decisions. When automation is supported by structured rules, reliable data, and machine learning, lenders can improve turnaround times while maintaining control over credit quality.
- Better Risk Assessment
Modern lending tools help lenders understand borrower risk with more depth than traditional data alone can provide. Credit bureau records, repayment history, income data, bank statements, alternative data, behavioural data, and device metadata can all support a more complete view of an applicant. This is especially useful when borrowers have limited traditional credit history or when market conditions change quickly.
Machine learning algorithms can analyse large volumes of structured and non-traditional data to identify patterns that manual review may miss. As a result, lenders can make more consistent, data-driven decisions and price risk with greater confidence.
- Risk Reduction
Risk reduction focuses on what happens before, during, and after a lending decision. Strong lending technology helps organisations monitor applications, transactions, behavioural interactions, and portfolio activity for anomalies that may indicate higher exposure. When suspicious patterns are identified early, lenders can trigger additional checks, adjust workflows, or escalate cases for review.
Reduced risk and reduced fraudulent attempts are closely connected because organisations that constantly monitor anomalies make misuse harder, easier to detect, and less rewarding. This creates stronger protection across the full lending lifecycle without slowing every borrower down.
- Financial Inclusion
A major advantage of lending technology is its ability to support more inclusive credit decisions. Many consumers and small businesses are financially active but remain underserved because they have thin credit files, limited formal borrowing history, or inconsistent access to traditional financial products.
By using alternative credit scoring, behavioural scoring, and other forms of alternative data, lenders can assess creditworthiness beyond standard bureau-based signals. This helps responsible borrowers who may otherwise be rejected too early in the process. In 2026, financial inclusion is not only a social priority. It is also a growth opportunity for lenders.
- Operational Efficiency
Lending teams are under pressure to process more applications, control costs, manage risk, and maintain a smooth borrower experience. Technology helps by streamlining automated workflows across onboarding, document collection, verification, underwriting, approval, monitoring, and reporting. This reduces repetitive manual work and allows teams to focus on complex cases where human judgement adds the most value.
It also improves consistency because decisions are guided by defined rules, data models, and audit-ready processes. For growing lenders, operational efficiency is critical because it allows them to scale loan volumes without increasing headcount at the same pace.
Traditional vs. Digital Lending Tools
Credit Risk Solutions
Credolab leverages anonymous and user-consented device and behavioural interactions metadata to generate digital scorecards, with its proprietary credit score serving as the company’s core product. Credolab calculates its credit score as a statistical measurement of an applicant’s relative credit risk profile.
Similar to a credit score from a credit bureau, the higher the Credolab credit score, the lower the probability of default.
Credolab can also create other scorecards, including intent (or approval) scores, indicating a user’s probability of buying or applying for a product. This allows clients to cross-sell more accurately and retain the stickiness of good-performing customers.
With this solution, lenders can analyse creditworthiness more accurately in real-time, maintaining regulation compliance, given that Credolab uses only privacy-consented and permissioned metadata collected anonymously from the financial institution client website and mobile app.
LexisNexis is an identity authentication and verification solution that manages the information from more than 276 million identities in the United States. Since they also use non-personally identifiable information, this tool can fight fraud and mitigate risk while remaining compliant.
GBG’s goal is to ensure the health of the onboarding process by verifying customer identity and keeping fraudsters out without compromising the company’s compliance obligations. GBG builds a secure and fast customer onboarding experience that runs smoothly with low friction.
Moody’s Analytics provides lenders with the tools to measure, manage, and mitigate their loan and investment portfolios’ credit risk through their credit risk solutions.
One of Moody’s Analytics’ solutions is their credit risk modelling, which helps lenders to cassess and manage current and future credit risk exposures across different types of assets. At the same time, it can support origination, risk management, compliance, and strategic objectives.
This company helps leaders improve their decision-making process with open banking credit scoring.
Algoan’s Open Banking-based solutions credit decisions are based on factual, comprehensive, and up-to-date information. Consequently, open banking becomes more inclusive and accountable by assisting underserved populations and preventing over-indebtedness.
Fraud Solutions
Crowe Global, a public accounting, consulting and technology firm, reported in its previous research that fraud costs businesses and individuals worldwide US$5.127 trillion annually.
Therefore, besides credit scoring solutions, having the right tools to fight against scams is a must to prevent irreversible damage.
Credolab uses real-time device and behavioural intelligence to identify patterns associated with higher-risk activity during onboarding. By analysing proprietary interaction metadata, Credolab helps lenders assess whether a device displays characteristics similar to those previously linked to confirmed cases of misuse.
Device velocity helps lenders flag potential fraudulent applications by analysing the rate at which users submit multiple applications with the same device within a certain time window. In other words, Credolab helps identify if the same device applies to multiple loans/credit cards using different identities, personal data, or SIM cards.
Without this fraud solution, banks are left exposed to organised criminal attacks that leverage fake identities to game the system and get a loan approved without any intention to repay it. The higher the fraud score, the more likely the user will be flagged as fraudulent.
Using our fraud score, clients can detect atypical behaviours based on depersonalised behavioural data.
The company’s objective is to protect shoppers against fraud by providing true identity recognition and discovering customer payment intent. The solution identifies who is behind the transaction, blocking any fraudulent behaviours.
Seon’s solution helps companies to discover fraud patterns and revenue opportunities through real-time data analysis. It combines digital and social media information, phone, email, Internet Protocol (IP), and device lookups, with ML technology. Seon adapts to the different ways businesses evaluate risk.
Sift combines technology and its global data network to avoid scams, reduce false positives and power frictionless experiences. The Sift solution protects you from fraudulent payments, fake accounts, spam, account takeover and disputes.
Kount’s AI (Artificial Intelligence) driven Identity Trust Platform protects every interaction across the whole customer journey.
Through hands-free automation and flexible controls, businesses could make more accurate decisions and increase approval rates, reduce chargebacks, stop bot attacks, prevent account takeover, and manage disputes.
This TransUnion solution validates consumer and device identities to prevent fraud without interfering with the user experience. In addition, this tool provides businesses with insights into consumer transactions using traditional data science and ML.
Marketing Solutions
Every day, customers are exposed to several offers. It is crucial to have a deep knowledge of who they are and what they are interested in. This will enable you to target these potential clients with tailored messages and products.
Marketing solutions based on data and ML technologies can provide profitable insights, capable of increasing acquisition and retention rates and are a reliable ally for any company.
Credolab analyses the behavioural interactions of a user’s device and provides granular, updated, and predictive insights to its clients in the form of features. Features contain a wealth of information and insights and are delivered via a single Application Programming Interface (API), allowing clients to build personas, not segments.
Some examples of these features include device brand/model info, app information (e.g. the number of gambling apps installed, existing competitor(s) apps installed), how calendar events are scheduled, how contacts are saved, and many more.
Using these findings, Credolab helps clients better understand their own customers and improve how they target prospects who exhibit similar persona traits.
Twilio’s Customer Data Platform (CDP) unifies all the customers’ touch points across all platforms and channels, providing companies with a complete understanding of their customer journey. Therefore, businesses can offer personalised and consistent real-time customer experiences.
The Neustar solution helps businesses improve marketing planning and performance across the customer journey. They provide updated and enriched insights, so companies gain a deeper understanding of their customers.
Zoho CRM is customer relationship management software that helps businesses obtain more leads and engage customers at the right time with the right message. With their solution, businesses can build tailored customer experiences.
Google Ads Data Hub (ADH) allows businesses to access marketing data from Google, YouTube and DV360 and develop a tailored analysis according to their business objectives, respecting customer privacy.
Winning market share is not an easy task for lenders. There is a lot of competition, and consumers have become more selective. Clients demand not only better products and services but also better customer experiences.
Besides, wrong decisions related to compliance, fraud and risk could cause irreversible damage to businesses. Therefore, choosing the right solution for a business could make the difference between success and failure.
How to Choose the Right Lending System Tools
Begin with a structured scorecard of must-have features for lending system tools and undertake rigorous vendor due diligence, using this framework by Oliver Wyman to identify effective lending tools that are both scalable and compliant.
Coverage and specificity
Select tools capable of assessing 100% of digital applicants while capturing granular, individual-level signals, with behavioural biometrics and device metadata proving particularly effective in this regard.
Accuracy and timeliness
Prioritise solutions that collect data contextually at the point of application rather than relying on outdated batch feeds, as real-time Software Development Kits (SDKs) provide superior accuracy and efficiency.
Orthogonality to Bureau Data
Focus on signals that genuinely contribute new information rather than replicating bureau data, thereby enhancing model performance through stronger AUC/Gini (Area Under the ROC Curve) scores and improved predictive accuracy.
Predictive Power and Machine Learning
Engage vendors that deploy supervised and unsupervised ML models to dynamically interpret behavioural patterns, offering greater adaptability than traditional, rule-based systems.
Regulatory compliance & privacy
Ensure that any solution leverages first-party, consented, and anonymised metadata with transparent usage notices, thereby safeguarding compliance obligations and preventing the exposure of Personally Identifiable Information (PII).
Depth of Protection
Evaluate whether the tools cover device integrity, virtualisation and emulation detection, proxy and spoofing checks, as well as velocity and bot alerts, all of which are essential for comprehensive fraud defence.
Analytics for new verticals
Institutions operating in crypto-secured or tokenised finance should adopt tools to analyse loan metrics in digital assets lending alongside traditional scorecards to benchmark collateral health, Loan-to-Value (LTV), and liquidation risk.
It is equally important to deploy tools for lenders to assess borrower assets when collateral encompasses bank accounts, digital wallets, and other alternative holdings.
Discovery and Benchmarking
Incorporate analyst grids and due diligence frameworks to identify the best tools for identifying leading lending and borrowing platforms, ensuring that chosen lending system tools maintain competitiveness as markets evolve.
Tip: Rather than depending on a single source of alternative data, blend multiple datasets to achieve stability and fairness, validate them through coverage and specificity checks, and consistently select effective lending tools that are explainable, auditable, and capable of scaling with institutional needs.
How Credolab Fits Into Your Lending Technology Stack
Credolab fits into a lender’s technology stack as an additional behavioural intelligence layer that strengthens the tools already used across the credit lifecycle.
Credit bureau data remains valuable for understanding repayment history and traditional credit behaviour, while Credolab adds behavioural risk scoring based on device and behavioural metadata to improve predictive power beyond traditional data alone.
ID verification tools only help confirm who the applicant is, while Credolab adds further context by analysing behavioural interactions during the application session to identify what they are doing, any suspicious patterns and support smoother automated workflows.
Fraud detection tools help lenders protect the application process, while Credolab strengthens this layer with behavioural and device intelligence that can highlight anomalies earlier, reduce exposure, and support stronger onboarding controls without adding unnecessary friction for genuine applicants.
FAQs
What tools help lenders see the full picture of a borrower's assets?
Some of these tools for lenders are asset verification tools, open banking platforms, bank statement analysers, credit bureau data, and alternative data tools help lenders understand a borrower’s assets, liabilities, cash flow, and repayment capacity.
What lending technology tools help improve underwriting?
Credit scoring tools, loan origination systems, income verification tools, alternative credit scoring platforms, open banking solutions, and machine learning-based decision engines help lenders improve underwriting accuracy.
What are the best decisioning tools for marketplace lenders?
The best decisioning tools for marketplace lenders include automated decision engines, alternative risk scoring tools, identity verification platforms, credit bureau integrations, open banking tools, and portfolio monitoring systems.
What is the difference between a loan origination system and a loan management system?
A loan origination system manages the application, verification, underwriting, and approval process, while a loan management system handles servicing, repayments, collections, and ongoing account administration.
How do alternative data tools improve lending decisions?
Alternative data tools improve lending decisions by adding non-traditional data, behavioural data, and device metadata that help lenders assess borrowers with limited or incomplete traditional credit history.
How does Credolab integrate into a lender's technology stack?
Credolab integrates as an added behavioural intelligence layer that works with credit bureau data, ID verification, and fraud detection tools to strengthen credit decisions and reduce risk.
How does alternative data improve lending decisions?
Alternative data improves lending decisions by giving lenders more context beyond traditional data, helping them better assess affordability, risk, and creditworthiness across a wider borrower base.
What are the best tools for identifying leading lending and borrowing platforms?
The best tools for lenders for identifying leading lending and borrowing platforms include market intelligence platforms, fintech directories, app analytics tools, industry databases, review platforms, and other trusted sources for credit research for lenders which help identify leading lending and borrowing platforms.
What tools help lenders see the full picture of a borrower’s assets?
For lenders asking, “what tools help lenders see the full picture of a borrower’s assets?” open banking platforms, asset verification tools, income verification systems, credit bureau data, and alternative data tools provide a clearer view of the borrower’s financial position.
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