Financial Services

Our State of Alternative Data Report provides insight into the alternative lending market, new data sources and growth opportunities.

The gig economy — also called the sharing economy or access economy — is an activity where people earn income by providing on-demand work, services, or goods. Often, it is through a digital platform like an application (app) or website. The gig economy seamlessly connects individuals with a diverse range of services, whether it be a skilled handyman for those long-awaited office shelves, or an experienced chauffeur to quickly drive you to the airport to not miss your flight. However, there are instances when these arrangements fall short of expectations. The hired handyman may send a substitute who’s ill-equipped for the task, or the experienced driver takes the wrong shortcut leaving you scrambling to make your flight on time. On the flip side, there are numerous risks faced by those working in the gig/sharing economy, from irritable customers to dangerous situations. In such cases, trust takes a hit. The gig economy has witnessed a surge in recent years, as individuals gravitate towards flexible, freelance, and contract work instead of traditional full-time employment. This shift has unlocked a multitude of opportunities for both workers and businesses. Nevertheless, it has also ushered in challenges pertaining to security and trust. One such challenge revolves around the escalating significance of digital identity verification within the gig economy.  Digital identity verification and the gig economy  Digital identity verification encompasses validating a person's identity through digital means, such as biometric data, facial recognition, or document verification. Within the gig economy, this process has high importance, as it establishes trust between businesses and their pool of freelance or contract workers. With the escalating number of remote workers and the proliferation of online platforms connecting businesses with gig workers, verifying the identities of these individuals has become more vital than ever before.  Protecting gig users and improving the customer experience   One primary rationale behind the mounting importance of digital identity verification in the gig economy is its role in curbing fraud. As the gig economy gains traction, the risk of individuals misrepresenting themselves or their qualifications to secure work burgeons. This scenario can lead businesses to hire unqualified or even fraudulent workers, thereby posing severe repercussions for both the company and its customers. By adopting digital identity verification processes, businesses can ensure the legitimacy and competence of their workforce, subsequently decreasing the risk of fraudulent activities.  In the digital age, trust and safety are crucial for businesses to succeed. Consumers prioritize brands they can trust, and broken trust can lead to loss of customers.According to Experian's 2023 Fraud and Identity Report, over 52% of US consumers feel they’re more of a target for online fraud than they were a year ago. As such, online security continues to be a real concern for most consumers. Nearly 64% of consumers say that they are very or somewhat concerned with online security, with 32% saying they are very concerned. Establishing trust and safety measures not only protects your brand but also enhances the user experience, fosters loyalty, and boosts your business.    Role of a dedicated Trust and safety team  Trust and safety are the set of business practices for online platforms to follow to reduce the risk of users being exposed to harm, fraud, or other behaviors outside community guidelines. This is becoming an increasingly important function as online platforms look to protect their users while improving customer acquisition, engagement, and retention.  That team also safeguards organizations from security threats and scams. They verify customers' identities, evaluate actions and intentions, and ensure a safe environment for all platform users. This enables both organizations and customers to trust each other and have confidence in the platform. Their role has evolved from fraud prevention to encompass broader areas, such as user-generated content and the metaverse. With the rise of user-generated content, platforms face challenges like fake accounts, imitations, malicious links, and inappropriate content. As a result, trust and safety teams have expanded their focus and are involved in product engineering and customer journey design. Another noteworthy factor contributing to the growing emphasis on digital identity verification for trust and safety teams stems from the necessity to adhere to diverse regulations and laws. Many countries have implemented stringent regulations to safeguard workers and ensure the legal and ethical operations of businesses. In the United States, for instance, businesses must verify the identities and work eligibility of all employees, including freelancers and contractors, as part of the Form I-9 process. By leveraging digital identity verification tools, businesses can streamline these procedures and guarantee compliance with prevailing regulations.  Mitigating risk in online marketplaces  To mitigate risks in online marketplaces, businesses can take several steps, including creating a clear set of user guidelines, implementing identity verification during onboarding, enforcing multi-factor authentication for all accounts, leveraging reverification during high-risk moments, performing link analysis on the user base, and applying automation. Online identity verification plays a pivotal role in safeguarding gig workers themselves. With the surge of online platforms connecting businesses with freelancers and contractors, there comes an augmented risk of workers falling prey to scams or identity theft. By mandating digital identity verification as an integral part of the onboarding process, these platforms can shield workers and ensure they only engage with bona fide businesses.  While automation can be a powerful tool for fraud detection and mitigation, it is not a cure-all solution. Automated identity verification has its strengths, but it also has its weaknesses. While automation can spot risk signals that a human might miss, a human might spot risk signals that automation would have skipped. Therefore, for many companies, the goal should not be full automation but achieving the right ratio of automation to manual review. Manual review takes time, but it's necessary to ensure that all potential risks are identified and addressed. The more efficient these processes can be, the better, as it allows for a quicker response to potential threats. As the number of individuals embracing freelance and contract work surges, and businesses increasingly rely on these workers to carry out vital responsibilities, ensuring the security and trustworthiness of these individuals becomes paramount. By integrating digital identity verification processes, businesses can shield themselves against fraud, comply with regulations, and cultivate trust with their gig workers.  Finding the right partner While trust and safety are concerns for all online marketplaces, there’s no universal solution that will apply to all businesses and in all cases. Your trust and safety policies need to be tailored to the realities of your business. The industries you serve, regions you operate in, regulations you are subject to, and expectations of your users should all inform your processes. Experian’s comprehensive suite of customizable identity verification solutions can help you solve the problem of trust and safety once and for all.  Learn more *This article leverages/includes content created by an AI language model and is intended to provide general information.

Automated debt collection can help you save time and money while increasing customer satisfaction and long-term values.

Explore what multi-factor authentication is, how it works, and why it’s crucial for enhancing your digital security against data breaches.

  Managing digital identities is a necessity, responsibility and privilege. When done right, digital identity management solutions can help consumers feel recognized and safe. In turn, companies can build strong and personalized relationships with their customers while complying with regulatory requirements and combating hydra-like fraud attacks. What is digital identity? The concept and definition of a digital identity have expanded as everyday interactions increasingly happen in digital realms. Today, a digital identity is more than an online account. Identities can be created and depend on all the digital information associated with a unique entity, which may be a person, business or device. A person's digital identity often includes online and offline attributes that fall into one of three categories: Something a user knows, such as a username, password or PIN. Something a user has, such as a mobile phone or security token. Something that's part of the user, such as a fingerprint, iris, voice pattern, behavior or preferences. People are increasingly open to sharing this type of personal information if it serves a purpose. Our Global Identity and Fraud Report found that 57 percent of consumers are willing to share data if it ensures greater security or prevents fraud, and 63 percent of consumers think sharing data is beneficial (up from 51 percent in 2021).1 People can also use these identifiers to verify their identity at a later point. But digital identity verification tools should rely on more than user-provided verification alone. A person may have hundreds or thousands of digital interactions every day, and these can leave digital footprints that you can use to create or expand digital identities. These types of identifiers — such as search queries, geotags, behaviors and device information — can also help you authenticate a user and offer a more customized and seamless experience. However, when focusing on consumers' digital identities, it's important to remember that their identity is more than the sum of data points. A person's digital identity is unique and personal, and it should be managed accordingly. The business side's challenges A discussion of what makes up an identity can quickly turn philosophical. For instance, you can't authenticate identical twins based on a face scan or DNA test, so what is it that makes them unique? In some ways, the example gets to the heart of businesses' challenges today. To create a safe and enjoyable online identity verification experience, you need to be able to distinguish between a real person and an imitator, even when the two look nearly identical. Access to more information can make this easier, but you then need to ensure that you can keep this information secure. It can be a tricky balance, but if you get it right, your efforts will be rewarded. People want to be recognized as they move across channels and devices, and organizations want to be able to quickly and accurately identify users with a friction-right experience that also helps prevent fraud. However, while 84 percent of businesses say recognizing customers is "very" or "extremely" important, only about 33 percent of consumers are confident that they'll be repeatedly recognized online.1 There's a clear gap — and an opportunity to better meet customers' desires. Organizations across industries know they need a customer recognition strategy and 82% already have one in place.2 Some businesses address this challenge with identity platforms that are standardized and interoperable. Standardization allows the platform to gather and store the growing influx of data that it can use as part of a digital identity strategy. Interoperability allows the platform to match different types of data, including physical data, with a person to verify their digital identity and avoid the creation of duplicate identities. In short, the platforms can make sense of increasingly large amounts of internal and external data and easily incorporate new data sources as they become available. Regulatory compliance and digital identity Navigating the regulatory landscape is a significant challenge for organizations dealing with digital identities. Compliance is not only necessary for legal reasons but also critical to maintaining customer trust and safeguarding institutional reputation. Organizations must stay informed about the regulatory frameworks that affect digital identity, such as the General Data Protection Regulation (GDPR) in the European Union, the California Consumer Privacy Act (CCPA), and other pertinent laws in jurisdictions they operate. These regulations dictate how personal data can be collected, stored, used and shared. Staying ahead of regulatory changes: Regulatory landscapes are dynamic, particularly concerning digital data. Organizations should engage with policymakers and participate in industry forums to  stay ahead of changes. By proactively managing compliance, organizations can avoid costly penalties, operational disruptions and reputational damage. The consumer's perspective Some organizations are adopting a consumer-centric approach to digital identity that puts consumers' needs and desires first. These can broadly be broken into four categories: Security: While people want a seamless and personalized experience, security and privacy are listed as top concerns year after year.1 That might not be surprising given that data breaches continually make headlines and there are growing concerns over identity theft. Privacy: Security is related to privacy, but privacy means more than keeping consumers' information safe from hackers. Our April 2022 Global Insight Report found that 90 percent of consumers want some or complete control over how their personal data is used. 3 Recognition: People want to be continually recognized once they share and verify their identity, even if they move between devices or channels. And nearly 70 percent of consumers say it's important for businesses to recognize them across multiple visits.1 Inclusion: Consumers may have varying levels of access to technology, comfort with technology and access to physical identifiers. Creating digital identity solutions for these potential barriers can also increase financial inclusion. While these are all areas of focus, organizations also need to find the right fit for each person and interaction. For instance, consumers may expect and even appreciate a robust verification process when they're opening a new financial account. But they could quickly be turned off by a similar process if they're making a small purchase or trying to play a new online game. What to look for in a digital identity partner Digital identity solutions and services have grown increasingly sophisticated to meet today's challenges. Identity hubs and data orchestration engines can connect with multiple services to help create, resolve, verify and authenticate identities. By moving away from a siloed approach, businesses can offer customers a better experience while minimizing their risk throughout the customer journey. When comparing potential partners, look for a company that: Has a customer-first approach: If your business is customer-first, then you need a partner who has a similar view. Uses multidimensional data: The partner should be able to offer and use offline and digital data sources to resolve, verify and authenticate digital identities. Its capabilities may become increasingly important as new data sources emerge. Isn't afraid to innovate: Look into how the partner is testing and using the latest advancements, such as artificial intelligence, in its digital identity solutions. Protects your brand: Understand how the partner helps detect and prevent fraud while creating a seamless experience for your customers and protecting their data. The right partner can increase your bottom line, help you build trust and improve your brand's reputation. Learn more about Experian Identity, an integrated approach to digital identity that builds on Experian's decades of experience managing and securing identifying information. Learn more 1“2022 Global Identity and Fraud Report: Building digital consumer trust amidst rising fraud activity and concerns," Experian, June 2022 2“2021 Global Identity and Fraud Report: Protecting and enabling customer engagements in the new digital era," Experian, April 2021. https://www.experian.com/content/dam/marketing/na/global-da/pdfs/GIDFR_2022.pdf https://www.experian.co.th/wp-content/uploads/2021/04/Experian-Global-Identity-Fraud-Report-2021.pdf 3"Global Insights Report: April 2022," Experian, April 2022. https://www.experian.com/blogs/global-insights/wp-content/uploads/2022/04/WaveReportApril2022.pdf *This article includes content created by an AI language model and is intended to provide general information.

Third-party fraud involves an identifiable victim that is willing to collaborate in the investigation and resolution.

Account takeover fraud can be costly, but is preventable with the right account takeover fraud prevention solution.

For companies that regularly engage in financial transactions, having a customer identification program (CIP) is mandatory to comply with the regulations around identity verification requirements across the customer lifecycle. In this blog post, we will delve into the essentials of a customer identification program, what it entails, and why it is important for businesses to implement one. What is a customer identification program? A CIP is a set of procedures implemented by financial institutions to verify the identity of their customers. The purpose of a CIP is to be a part of a financial institution’s fraud management solutions, with similar goals as to detect and prevent fraud like money laundering, identity theft, and other fraudulent activities. The program enables financial institutions to assess the risk level associated with a particular customer and determine whether their business dealings are legitimate. An effective CIP program should check the following boxes: Confidently verify customer identities Seamless authentication Understand and anticipate customer activities Where does Know Your Customer (KYC) fit in? KYC policies must include a robust CIP across the customer lifecycle from initial onboarding through portfolio management. KYC solutions encompass the financial institution’s customer identification program, customer due diligence and ongoing monitoring. What are the requirements for a CIP? Customer identification program requirements vary depending on the type of financial institution, the type of account opened, and other factors. However, the essential components of a CIP include verifying the customer's identity using government-issued identification, obtaining and verifying the customer's address, and checking the customer against a list of known criminals, terrorists, or suspicious individuals. These measures  help detect and prevent financial crimes. Why is a CIP important for businesses? CIP helps businesses mitigate risk by ensuring they have accurate and up-to-date information about their customers. This also helps financial institutions comply with laws and regulations that require them to monitor financial transactions for any suspicious activities. By having a robust CIP in place, businesses can establish trust and rapport with their customers. According to Experian’s 2024 U.S. Identity and Fraud Report, 63% of consumers say it's extremely or very important for businesses to recognize them online. Having an effective CIP in place is part of financial institutions showing their consumers that they have their best interests top of mind. Finding the right partner It’s important to find a partner you trust when working to establish processes and procedures for verifying customer identity, address, and other relevant information. Companies can also utilize specialized software that can help streamline the CIP process and ensure that it is being carried out accurately and consistently. Experian’s proprietary and partner data sources and flexible monitoring and segmentation tools allow you to resolve CIP discrepancies and fraud risk in a single step, all while keeping pace with emerging fraud threats with effective customer identification software. Putting consumers first is paramount. The security of their identity is priority one, but financial institutions must pay equal attention to their consumers’ preferences and experiences. It is not just enough to verify customer identities. Leading financial institutions will automate customer identification to reduce manual intervention and verify with a reasonable belief that the identity is valid and eligible to use the services you provide. Seamless experiences with the right amount of friction (I.e., multi-factor authentication) should also be pursued to preserve the quality of the customer experience. Putting it all together As cybersecurity threats are becoming more sophisticated, it is essential for financial institutions to protect their customerinformation and level up their fraud prevention solutions. Implementing a customer identification program is an essential component in achieving that objective. A robust CIP helps organizations detect, prevent, and deter fraudulent activities while ensuring compliance with regulatory requirements. While implementing a CIP can be complex, having a solid plan and establishing clear guidelines is the best way for companies to safeguard customer information and maintain their reputation. CIPs are an integral part of financial institutions security infrastructures and must be a business priority. By ensuring that they have accurate and up-to-date data on their customers, they can mitigate risk, establish trust, and comply with regulatory requirements. A sound CIP program can help financial institutions detect and prevent financial crimes and cyber threats while ensuring that legitimate business transactions are not disrupted, therefore safeguarding their customers' information and protecting their own reputation. Learn more

Fraud analytics can help your business keep up with sophisticated fraud attempts and provide expert security.

First-party fraud can be detected and prevented by using robust fraud risk management strategies and solutions.

Authorized push payment fraud is a growing threat. Learn how to detect and prevent it in our latest blog article. Read more!

Model governance is growing increasingly important as more companies implement machine learning model deployment and AI analytics solutions into their decision-making processes. Models are used by institutions to influence business decisions and identify risks based on data analysis and forecasting. While models do increase business efficiency, they also bring their own set of unique risks. Robust model governance can help mitigate these concerns, while still maintaining efficiency and a competitive edge. What is model governance? Model governance refers to the framework your organization has in place for overseeing how you manage your development, model deployment, validation and usage.1 This can involve policies like who has access to your models, how they are tested, how new versions are rolled out or how they are monitored for accuracy and bias.2 Because models analyze data and hypotheses to make predictions, there's inherent uncertainty in their forecasts.3 This uncertainty can sometimes make them vulnerable to errors, which makes robust governance so important. Machine learning model governance in banks, for example, might include internal controls, audits, a thorough inventory of models, proper documentation, oversight and ensuring transparent policies and procedures. One significant part of model governance is ensuring your business complies with federal regulations. The Federal Reserve Board and the Office of the Comptroller of the Currency (OCC) have published guidance protocols for how models are developed, implemented and used. Financial institutions that utilize models must ensure their internal policies are consistent with these regulations. The OCC requirements for financial institutions include: Model validations at least once a year Critical review by an independent party Proper model documentation Risk assessment of models' conceptual soundness, intended performance and comparisons to actual outcomes Vigorous validation procedures that mitigate risk Why is model governance important — especially now? More and more organizations are implementing AI, machine learning and analytics into their models. This means that in order to keep up with the competition's efficiency and accuracy, your business may need complex models as well. But as these models become more sophisticated, so does the need for robust governance.3 Undetected model errors can lead to financial loss, reputation damage and a host of other serious issues. These errors can be introduced at any point from design to implementation or even after deployment via inappropriate usage of the model, drift or other issues. With model governance, your organization can understand the intricacies of all the variables that can affect your models' results, controlling production closely with even greater efficiency and accuracy. Some common issues that model governance monitors for include:2 Testing for drift to ensure that accuracy is maintained over time. Ensuring models maintain accuracy if deployed in new locations or new demographics. Providing systems to continuously audit models for speed and accuracy. Identifying biases that may unintentionally creep into the model as it analyzes and learns from data. Ensuring transparency that meets federal regulations, rather than operating within a black box. Good model governance includes documentation that explains data sources and how decisions are reached. Model governance use cases Below are just three examples of use cases for model governance that can aid in advanced analytics solutions. Credit scoring A credit risk score can be used to help banks determine the risks of loans (and whether certain loans are approved at all). Governance can catch biases early, such as unintentionally only accepting lower credit scores from certain demographics. Audits can also catch biases for the bank that might result in a qualified applicant not getting a loan they should. Interest rate risk Governance can catch if a model is making interest rate errors, such as determining that a high-risk account is actually low-risk or vice versa. Sometimes changing market conditions, like a pandemic or recession, can unintentionally introduce errors into interest rate data analysis that governance will catch. Security challenges One department in a company might be utilizing a model specifically for their demographic to increase revenue, but if another department used the same model, they might be violating regulatory compliance.4 Governance can monitor model security and usage, ensuring compliance is maintained. Why Experian? Experian® provides risk mitigation tools and objective and comprehensive model risk management expertise that can help your company implement custom models, achieve robust governance and comply with any relevant federal regulations. In addition, Experian can provide customized modeling services that provide unique analytical insights to ensure your models are tailored to your specific needs. Experian's model risk governance services utilize business consultants with tenured experience who can provide expert independent, third-party reviews of your model risk management practices. Key services include: Back-testing and benchmarking: Experian validates performance and accuracy, including utilizing statistical metrics that compare your model's performance to previous years and industry benchmarks. Sensitivity analysis: While all models have some degree of uncertainty, Experian helps ensure your models still fall within the expected ranges of stability. Stress testing: Experian's experts will perform a series of characteristic-level stress tests to determine sensitivity to small changes and extreme changes. Gap analysis and action plan: Experts will provide a comprehensive gap analysis report with best-practice recommendations, including identifying discrepancies with regulatory requirements. Traditionally, model governance can be time-consuming and challenging, with numerous internal hurdles to overcome. Utilizing Experian's business intelligence and analytics solutions, alongside its model risk management expertise, allows clients to seamlessly pass requirements and experience accelerated implementation and deployment. Experian can optimize your model governance Experian is committed to helping you optimize your model governance and risk management. Learn more here. References 1Model Governance," Open Risk Manual, accessed September 29, 2023. https://www.openriskmanual.org/wiki/Model_Governance2Lorica, Ben, Doddi, Harish, and Talby, David. "What Are Model Governance and Model Operations?" O'Reilly, June 19, 2019. https://www.oreilly.com/radar/what-are-model-governance-and-model-operations/3"Comptroller's Handbook: Model Risk Management," Office of the Comptroller of the Currency. August 2021. https://www.occ.treas.gov/publications-and-resources/publications/comptrollers-handbook/files/model-risk-management/pub-ch-model-risk.pdf4Doddi, Harish. "What is AI Model Governance?" Forbes. August 2, 2021. https://www.forbes.com/sites/forbestechcouncil/2021/08/02/what-is-ai-model-governance/?sh=5f85335f15cd

Ghost student fraud is a serious and alarming issue in the educational sector. Learn how to spot it and safeguard your institution.

Data-driven machine learning model development is a critical strategy for financial institutions to stay ahead of their competition, and according to IDC, remains a strategic priority for technology buyers.  Improved operational efficiency, increased innovation, enhanced customer experiences and employee productivity are among the primary business objectives for organizations that choose to invest in artificial intelligence (AI) and machine learning (ML), according to IDC’s 2022 CEO survey.   While models have been around for some time, the volume of models and scale at which they are utilized has proliferated in recent years. Models are also now appearing in more regulated aspects of the business, which demand increased scrutiny and transparency.   Implementing an effective model development process is key to achieving business goals and complying with regulatory requirements. While ModelOps, the governance and life cycle management of a wide range of operationalized AI models, is becoming more popular, most organizations are still at relatively low levels of maturity. It's important for key stakeholders to implement best practices and accelerate the model development and deployment lifecycle.   Read the IDC Spotlight Challenges impeding machine learning model development  Model development involves many processes, from wrangling data, analysis, to building a model that is ready for deployment, that all need to be executed in a timely manner to ensure proper outcomes. However, it is challenging to manage all these processes in today’s complex environment.   Modeling challenges include:  Infrastructure: Necessary factors like storage and compute resources incur significant costs, which can keep organizations from evolving their machine learning capabilities.   Organizational: Implementing machine learning applications requires talent, like data scientists and data and machine learning engineers.  Operational: Piece meal approaches to ML tools and technologies can be cumbersome, especially on top of data being housed in different places across an organization, which can make pulling everything together challenging.  Opportunities for improvement are many While there are many places where individuals can focus on improving model development and deployment, there are a few key places where we see individuals experiencing some of the most time-consuming hang-ups.   Data wrangling and preparation   Respondents to IDC's 2022 AI StrategiesView Survey indicated that they spend nearly 22% of their time collecting and preparing data. Pinpointing the right data for the right purpose can be a big challenge. It is important for organizations to understand the entire data universe and effectively link external data sources with their own primary first party data. This way, stakeholders can have enough data that they trust to effectively train and build models.   Model building  While many tools have been developed in recent years to accelerate the actual building of models, the volume of models that often need to be built can be difficult given the many conflicting priorities for data teams within given institutions. Where possible, it is important for organizations to use templates or sophisticated platforms to ease the time to build a model and be able to repurpose elements that may already be working for other models within the business.   Improving Model Velocity Experian’s Ascend ML BuilderTM is an on-demand advanced model development environment optimized to support a specific project. Features include a dedicated environment, innovative compute optimization, pre-built code called ‘Accelerators’ that simply, guide, and speed data wrangling, common analyses and advanced modeling methods with the ability to add integrated deployment.  To learn more about Experian’s Ascend ML Builder, click here.   To read the full Technology Spotlight, download “Accelerating Model Velocity with a Flexible Machine Learning Model Development Environment for Financial Institutions” here.  Download spotlight *This article includes content created by an AI language model and is intended to provide general information. 

Fraudsters have evolved their techniques to capitalize on homeowners and lenders by shifting their focus from home purchases to HELOC fraud.