Data scientists, software engineers, and other experts are creating business solutions that rely on algorithms, automated decision-making tools, artificial intelligence (AI), and machine learning models. Their work has developed faster, more efficient business processes, but it has also raised some concerns.&nbsp;
<ul>
<li style="font-weight: 400;" aria-level="1">Washington state took measures to prevent abuse by <a href="https://demyst.com/blog/assessing-driver-risk-without-using-credit-scores">banning the use of credit information for setting insurance rates</a>.</li>
<li style="font-weight: 400;" aria-level="1">Lenders in New York need to provide fair and equal access to lending options and <a href="https://demyst.com/blog/the-increasing-responsibilities-of-non-bank-lenders">ensure that their practices are not discriminatory</a>.</li>
<li style="font-weight: 400;" aria-level="1">In Colorado, insurers who develop new insurance products must be prepared to <a href="https://demyst.com/blog/state-regulators-set-external-data-and-model-monitoring-requirements">explain the protections that they have in place</a> for avoiding unintended bias.</li>
</ul>
Regulators and consumers expect companies to demonstrate how they have mitigated bias and discriminatory decisions in their advanced decision-making models. Enterprises will need to explain how their work attempts to minimize these risks.
<h1>Data itself is neither good nor evil</h1>
In some models, data points like age or gender might be defined as &ldquo;bad&rdquo; and excluded to avoid biased outcomes. In other models, the same data points could be defined as &ldquo;good&rdquo; and integrated to produce unbiased outcomes. This ambiguity has been the source of public criticism, and faulty assumptions expose enterprises to serious consequences.
The advanced application of data offers a mix of threats and opportunities. Models and algorithms can reduce human error by interpreting data objectively, but they can also deploy human and societal biases at scale. In these cases, it is <a href="https://www.mckinsey.com/featured-insights/artificial-intelligence/tackling-bias-in-artificial-intelligence-and-in-humans">frequently the underlying data, not the algorithms themselves</a>, that cause problems.&nbsp;
Biased data not only presents risks to consumers, but also to the companies that serve them. Flawed pricing models can lead to financial losses, and regulators like the US Federal Trade Commission have warned that <a href="https://www.ftc.gov/business-guidance/blog/2021/04/aiming-truth-fairness-equity-your-companys-use-ai">companies will face regulatory action</a> if they fail to hold themselves accountable for the fairness of their algorithms.&nbsp;
However, organizations can integrate clean, equitable, and actively monitored external data into their workflows to improve digital onboarding, online fraud prevention, and credit decisioning processes &mdash; in addition to reducing model bias &mdash; to meet regulatory needs and customer concerns.&nbsp;
<h1>Discernment and Expertise</h1>
Market-leading enterprises overcome the limits of their own internal data by working with external data products. The key is to select expert partners who curate high-quality data that is relevant, cost-effective, and as close to real-time as possible.
Organizations like FinRegLab are exploring <a href="https://finreglab.org/alternative-data-in-underwriting-credit">how external data can be applied to credit underwriting decisions</a>. CGAP is researching emerging business models to determine which business models have <a href="https://www.cgap.org/topics/collections/fintech-future-of-banking">the clearest links to financial inclusion</a>. And Demyst has worked with TruEra to develop <a href="https://www.yahoo.com/now/truera-honored-fast-company-2022-113000836.html">a solution for mitigating bias in AI-based credit decisioning models</a>.
 Enterprises that commit to making a difference have seen instant value with immediate positive results. At the core level, these organizations are working with unbiased data and minimizing distortions so that their models account for a broader, more diverse population. <a href="https://demyst.com/sign-up">The effective integration of external data</a> is a key driver of digital transformation and external data platforms such as Demyst continue to work in partnership with innovative enterprises to help to make that difference.

Organizations can integrate clean, equitable, and actively monitored external data into their workflows to improve business outcomes.

Finding Solutions for Data Bias

Data-driven enterprises are using more data &mdash; sourced from external providers &mdash; to manage risk and compliance intelligently, improve customer experiences, automate common business processes, and power targeted marketing campaigns. However, it can be challenging to discover the right data, certify it for use, and deploy it into production.&nbsp;
Demyst and Snowflake have entered into a partnership that can address these challenges, delivering better customer outcomes by providing centralized access to <a href="https://demyst.com/blog/the-what-why-and-how-of-external-data/">an external data advisor</a> that guides the design and deployment of business solutions powered with data from multiple sources.
As a global network where thousands of organizations mobilize data with near-unlimited scale, concurrency, and performance, Snowflake delivers insights with an integrated service that leverages the power of cloud computing. Through the Snowflake Marketplace, Demyst now offers more than 40 Premium Data Collections drawing from multiple data providers, curated by Demyst experts, for defined use cases.
Digital transformation has led enterprises to rely on <a href="https://demyst.com/blog/external-datas-tipping-point/">external data for vital business processes</a>, and Snowflake customers can now deploy Demyst&rsquo;s Premium Data Collections for use cases that include <a href="https://www.snowflake.com/datasets/demyst-fraud-risk-premium-data-collection/">fraud prevention</a>, <a href="https://www.snowflake.com/datasets/demyst-electronic-identity-verification-eidv-premium-data-collection/">identity verification</a>, <a href="https://www.snowflake.com/datasets/demyst-business-financials-for-onboarding-premium-data-collection/">credit decisioning</a>, and <a href="https://www.snowflake.com/datasets/demyst-property-prefill-premium-data-collection/">application prefill</a> workflows for improved accuracy and fill rates.&nbsp;
<a href="https://demyst.com/blog/vendor-due-diligence-for-data-privacy/">Demyst&rsquo;s provider certification</a> ensures global data compliance, and deployment teams help customers get data into production faster through the expert curation of data that meets each customer&rsquo;s needs. These collections enable businesses to combine intelligence from thousands of data products to make faster and more accurate decisions that are informed by data.
Together, Demyst and Snowflake are enabling millions of global customers to benefit from rapid, seamless access to the combined insights of hundreds of data products that unlock faster, more efficient business decisions.
Demyst is a Magic Carpet Sponsor at the <a href="https://www.snowflake.com/summit/summit-partners/?_sf_s=demy#partner-190241">2022 Snowflake Summit</a> &mdash; connect with representatives at the event to discuss how the Demyst platform can provide API-ready solutions for some of the biggest business challenges.

Demyst and Snowflake can help millions of customers around the globe benefit from rapid, seamless access to combined insights from hundreds

Using Snowflake for External Data Deployment

Demyst and Snowflake can help millions of customers around the globe benefit from rapid, seamless access to combined insights from hundreds of data products.

Digital customers have high expectations, <a href="https://ffnews.com/newsarticle/new-research-from-tink-finds-88-of-consumers-abandon-payments-with-friction/">abandoning transactions at the first sign of friction</a>. At the same time, financial institutions need to comply with regulatory demands and control costs. These conflicting requirements are challenging to manage in an increasingly competitive landscape.
Todd Anderson, LendIt&rsquo;s chief product officer, convened a panel of financial services experts to discuss how successful products balance these priorities. His conversation with David Hicks, head of fraud at <a href="https://koalafi.com/">Koalafi</a>, and Brian Lanehart, president and co-founder of <a href="https://www.momnt.com/">Momnt</a>, explored their efforts to develop innovative products and the lessons they learned along the way.
&ldquo;People are really going from zero to one very fast, and they're actually able to stand up products with great customer experiences,&rdquo; Hicks said. New competitors in the financial space, who aren&rsquo;t slowed down by the legacy technical debt, can quickly tailor experiences to meet specific customer demands. &ldquo;It creates pressure for everybody.&rdquo;
Lanehart agreed about the combination of competitive pressure and technological advancement, which have driven innovative ways to offer new products. One example was Momnt&rsquo;s response to customer concerns about the global pandemic, which led them to develop a contactless application process.&nbsp;
Both Lanehart and Hicks applied external data to launch their products successfully, and the conversation with Anderson discussed the obstacles that they encountered along the way. By working with a partner who specialized in external data, Lanehart and Hicks were able to overcome major challenges that have prevented new financial products from getting off the ground:
<h1>1. Credit Bureau Data Delays&nbsp;</h1>
Although integration with credit bureaus has been described as long and tedious, Lanehart explained that it&rsquo;s essential &mdash; the majority of lenders are still using traditional credit metrics to determine who can access funds.&nbsp;
Some companies are exclusively using alternative data for credit decisioning, but Lanehart anticipates that it will be difficult for them to continue unless they integrate credit bureau data. &ldquo;The lending institution is so familiar with traditional data, you have to use it,&rdquo; Lanehart said. &ldquo;At least, as a start.&rdquo;&nbsp;
Integration delays can compound quickly if lenders want to draw data from multiple bureaus that require separate integration processes. These development cycles can be shortened by working with partners who specialize in external data and who have already integrated their systems with credit bureaus. The partnerships enable better management, faster iteration, and more positive consumer outcomes.
&ldquo;As a startup, there's no way we could have gotten to market with a beta product in six months without someone like Demyst,&rdquo; Lanehart said. &ldquo;It would take forever to go through every single credit bureau.&rdquo;
<h1>2. Integrating Low-Value Data Sources</h1>
Hicks was enthusiastic about new products that merge data from multiple sources. &ldquo;If you look at the spectrum of data out there, and you think about external data and internal data, you certainly want to have both,&rdquo; he said. &ldquo;The credit score that we're all used to seeing is designed to satisfy all kinds of different needs and purposes that are similar, but not exactly the same.&rdquo;
However, It's important to understand which external data products will be useful. Lanehart said, &ldquo;That was a big challenge: is the data valuable?&rdquo; He stressed the importance of working with an external data partner who can recommend data sources based on a deep understanding of existing products.&nbsp;
&ldquo;If you go straight to a data provider,&rdquo; Lanehart said, &ldquo;they&rsquo;ll do everything they can to sell you,&rdquo; He added, &ldquo;There's no way we could become experts on all the available data that's out there.&rdquo;
Hicks also pointed out the opportunity costs involved in integration. &ldquo;Tech capacity is a scarce resource,&rdquo; he said. &ldquo;I don&rsquo;t want to do a thousand different integrations to actually figure out who I want to use.&rdquo;&nbsp;
<h1>3. Competing in Crowded Markets&nbsp;</h1>
The data from established credit bureaus &mdash; like <a href="https://demyst.com/app/data-catalog/connectors?page=1&amp;search=experian&amp;utm_medium=blog&amp;utm_source=bpst">Experian</a>, <a href="https://demyst.com/app/data-catalog/connectors?page=1&amp;search=equifax&amp;utm_medium=blog&amp;utm_source=bpst">Equifax</a>, and <a href="https://demyst.com/app/data-catalog/connectors?page=1&amp;search=transunion&amp;utm_medium=blog&amp;utm_source=bpst">TransUnion</a> &mdash; has created an environment where lenders compete for the same customers on the same terms because they have the same credit scores. By looking beyond traditional data, Koalafi and Momnt were able to identify new opportunities; their richer understanding of consumer risk profiles identified customers whose scores were likely to change.&nbsp;
&ldquo;Using alternative data &mdash; from Demyst, for example &mdash; we can see that a 640 is behaving like a 720,&rdquo; Lanehart said, &ldquo;even though their score is 640.&rdquo; These insights allow lenders to adjust credit offers in ways that benefit consumers that are overlooked by traditional credit models.&nbsp;
To identify these opportunities, lenders need to integrate multiple sources of data. &ldquo;It's very tricky, when you're out there working with all those different vendors, to make sure that you're going to be able to get an apples to apples read,&rdquo; Hicks said. &ldquo;If you use a centralized party, it does create a little bit of scalability naturally.&rdquo;
<h1>4. Ignoring Algorithmic Bias</h1>
Apple and Goldman Sachs encountered <a href="https://techcrunch.com/2021/08/14/how-the-law-got-it-wrong-with-apple-card/">difficulties with their 2019 credit card program</a> that served as a warning for lenders who develop new credit products: regulators are paying attention to algorithmic bias.
Lanehart explained that Momnt takes these concerns seriously. &ldquo;We're very specific, and we're very intentional as we feed data into our credit policy: Is there anything in here that would ever be considered biased data? Whether it's from our perspective, or a regulator's perspective, or most importantly, a consumer&rsquo;s perspective.&rdquo;
Regulatory monitoring upholds standards and governance across financial products, and Lanehart welcomed it. &ldquo;We&rsquo;ve been through SOC 2, we&rsquo;ve prepared hundreds of pages of documentation as we built the platform, we know what the bank regulators are gonna want.&rdquo;
Ultimately, new financial products will need to explain how their models use data, and they will need to <a href="https://marketing.truera.com/external-data-and-ai-quality-management-white-paper">identify and avoid biased data</a>. &ldquo;Demyst is a good partner in that they&rsquo;ll tell us &mdash; because they're seeing the data being used in so many different contexts,&rdquo; Lanehart said. &ldquo;Like us, they&rsquo;re also watching the regulatory news, and they&rsquo;ll say &lsquo;there's a little bit of questionable data here.&rsquo;&rdquo;
<h1>5. Relying on Fragile Workflows</h1>
&ldquo;Your customer is not forgiving,&rdquo; Hicks said. &ldquo;They don't care if Equifax is down, or if any credit bureau is down.&rdquo; Instead of focusing on technical details, customers focus on the fact that they aren&rsquo;t getting what they want. &ldquo;That reflects on you as the actual lender.&rdquo;
Lenders need to meet these heightened expectations in an environment where <a href="https://demyst.com/blog/expecting-the-unexpected">data problems will happen</a>. &ldquo;People go down all the time,&rdquo; Hicks explained, &ldquo;even if it's just little blips here and there.&rdquo; Lenders can prepare for these setbacks with ongoing management of their data sources and an awareness of how those sources are performing.
&ldquo;If you use a company that is in the middle, it can actually help streamline that entire process,&rdquo; Hicks said. When lenders have a partner managing the orchestration layer for their financial products, it provides a single point of contact for identifying and correcting service outages.&nbsp;
<h1>Balancing Opportunities and Challenges</h1>
During Anderson&rsquo;s interview, Lanehart and Hicks explained how they successfully avoided these challenges and delivered innovative financial products to new markets. Anderson identified a common theme in their stories, noting that &ldquo;It's really about the combination of the various data.&rdquo;&nbsp;
&ldquo;Alternative data by itself is not going to get you there,&rdquo; Anderson said. &ldquo;In many cases, traditional data by itself is limited.&rdquo; But by merging alternative data and traditional data, lenders can compensate for shortcomings with greater flexibility that allows them to deliver more value to customers.&nbsp;
This places an emphasis on finding efficient ways to combine different sets of data. &ldquo;External data, no matter what, it&rsquo;s tricky,&rdquo; Hicks said. He pointed out that various data sources require discovery work, testing, and integration, followed by the transfer, storage, and ongoing management of the data.&nbsp;
&ldquo;There&rsquo;s a lot that&rsquo;s gonna go into that,&rdquo; Hicks said.&rdquo;No matter how you&rsquo;re going to tackle it, it is going to be a large undertaking.&rdquo; Finding the right partner for external data can make that undertaking more manageable.&nbsp;
The full video of Anderson&rsquo;s discussion is <a href="https://demyst.com/webinars/watch/XkOgsTKRTabmSyOwJq5F--overcome-external-data-deployment-challenges-to-launch-and-scale-financial-productions-featuring-brian-lanehart-momnt-and-david-hicks-koalafi??utm_medium=webinar&amp;utm_source=bpst&amp;gaAd=bpst">available on the Demyst website</a>. New users can also create accounts on the Demyst platform and explore ways to <a href="https://demyst.com/app/data-catalog/featured?utm_medium=blog&amp;utm_source=bpst">integrate new external data sources</a>.

Lenders need to merge alternative data with traditional data for the flexibility that allows them to deliver value.

5 Hurdles Facing New Financial Products

Data is the key to growth &mdash; it is vital for businesses to understand how they can harness and apply external data, because it provides an <a href="https://www.capgemini.com/gb-en/research/data-sharing-masters/">unbeatable competitive edge</a>.
<h1>What is external data?&nbsp;</h1>
External data comes from sources that are outside of an enterprise. It includes customer surveys, social media data, and third-party market research.
There are several ways that external data can drive business growth:
<ol>
<li style="font-weight: 400;" aria-level="1">Gaining unbiased insights into customers and target markets. </li>
<li style="font-weight: 400;" aria-level="1">Identifying new business opportunities. </li>
<li style="font-weight: 400;" aria-level="1">Tracking and measuring a company&rsquo;s progress over time. </li>
<li style="font-weight: 400;" aria-level="1">Benchmarking and measuring performance against other companies. </li>
<li style="font-weight: 400;" aria-level="1">Allocating resources more efficiently and enabling better decisions.</li>
</ol>
<h1>Why is external data necessary?&nbsp;</h1>
External data sources can provide essential information for businesses.
Insurance companies apply insights from external data to <a href="https://demyst.com/blog/looking-ahead-high-expectations-low-margins-for-error?utm_marketing_source=BPST&amp;gaAd=BPST">meet customer expectations</a>. External data can also be used to <a href="https://demyst.com/blog/assessing-driver-risk-without-using-credit-scores?utm_marketing_source=BPST&amp;gaAd=BPST">price auto insurance</a> more accurately.
In financial services, external data is part of ID verification processes for <a href="https://demyst.com/blog/digital-footprints-for-id-verification?utm_marketing_source=BPST&amp;gaAd=BPST">identifying and preventing fraudulent transactions</a>. Marketplace companies use external data to <a href="https://demyst.com/blog/improving-trust-on-managed-platforms?utm_marketing_source=BPST&amp;gaAd=BPST">maintain trust</a> and improve their onboarding workflows for new vendors. And <a href="https://demyst.com/blog/how-lenders-and-insurers-can-use-geolocation-data?utm_marketing_source=BPST&amp;gaAd=BPST">geolocation data</a> can provide a comprehensive view of business clients.
In other industries, external data is used to <a href="https://demyst.com/blog/penalties-mount-for-marketers-using-toxic-data?utm_marketing_source=BPST&amp;gaAd=BPST">improve marketing efforts</a> and <a href="https://demyst.com/blog/supply-chain-risk-management?utm_marketing_source=BPST&amp;gaAd=BPST">manage risks in the supply chain</a>.
<h1>How is external data accessed?</h1>
A data integration platform &mdash; also described as an orchestration layer &mdash; allows enterprises to connect with different data sources, combining that information in a central repository.&nbsp;
Data integration platforms need to make important decisions regarding data coverage, consistency, and compliance:
<ul>
<li style="font-weight: 400;" aria-level="1">Coverage: Some information may only be available from specific data providers. Other data providers may have more complete coverage in markets that are less relevant. </li>
<li style="font-weight: 400;" aria-level="1">Consistency: Data providers can present information in unique formats or sort identical properties under different metadata structures. Enterprises trying to compare equivalent data from multiple providers may need to perform additional <a href="https://demyst.com/blog/game-changer-demysts-entity-resolution-tech?utm_marketing_source=BPST&amp;gaAd=BPST">entity resolution</a> work. </li>
<li style="font-weight: 400;" aria-level="1">Compliance: Privacy considerations and the ethics of data collection processes should be carefully examined as part of <a href="https://demyst.com/blog/vendor-due-diligence-for-data-privacy">due diligence</a> efforts when integrating information from new sources.</li>
</ul>
Enterprises that build their own data integration platform must also allocate appropriate resources for the platform&rsquo;s ongoing maintenance and support.
<h1>Does every company need external data capabilities?</h1>
External data provides access to new markets, new customers, and new insights for making effective decisions. However, enterprises need to weigh the benefits of external data against the costs of managing their own data integration platforms.
Data exchanges like the <a href="https://resources.snowflake.com/solution-briefs/improve-data-analytics-with-demystdata-and-snowflake">Snowflake Data Marketplace</a>, the <a href="https://partners.amazonaws.com/partners/001E000001d9PSOIA2/DemystData">AWS Data Exchange</a>, and <a href="https://demyst.com/platform?utm_marketing_source=BPST&amp;gaAd=BPST">the Demyst platform</a> can offer cost-effective access to external data. Organizations can also work with partners who have already developed external data expertise and made the necessary investments in data infrastructure.
Without access to external data, companies are limited in their ability to make decisions, find new opportunities, and keep up with competitors. The relevant question is not whether organizations need external data, but whether they need to build their own data infrastructure and manage relationships with multiple data providers.
By working with Demyst, enterprises can access <a href="https://demyst.com/platform">a single global interface</a> for discovering, building, and deploying the right external data solutions.

External data provides access to new markets, new customers, and new insights for making effective decisions.

The What, Why, and How of External Data

When external data is paired with machine learning, the results can change the world. The potential of such partnerships was recognized by Fast Company&rsquo;s <a href="https://www.fastcompany.com/90742276/world-changing-ideas-awards-2022-ai-and-data-finalists-and-honorable-mentions" rel="follow">World Changing Ideas 2022</a> list, which honored Demyst and TruEra for their work using AI and data to empower positive change.
TruEra and Demyst worked together to develop a solution to mitigate bias in AI-based credit decisioning models, such as those used by financial services companies, demonstrating how external data can be a key part of driving <a href="https://marketing.truera.com/external-data-and-ai-quality-management-white-paper" rel="follow">new and effective machine learning use cases</a>. This will improve applications that are based on machine learning models, which can be held back by model performance or bias issues, particularly in regulated industries.
Scott Albin, Chief Strategy Officer at Demyst, said, &ldquo;We are proud to be recognized by Fast Company. It&rsquo;s a clear demonstration that machine learning use cases can be both expanded and dramatically improved by the use of high quality, curated, and compliant external data.&rdquo;
Collaboration between TruEra and Demyst previously showed how third-party data and AI quality management solutions can work together to improve the accuracy and fairness of credit decisioning models. Their project won the <a href="https://finance.yahoo.com/news/truera-demyst-win-codifying-responsible-130000175.html" rel="follow">Global Veritas Challenge 2021 at the Singapore Fintech Festival</a>, the world&rsquo;s largest fintech event.
Demyst already accelerates the deployment of external data solutions for the world&rsquo;s leading banks, insurers, and fintechs. The work with TruEra has developed a clear and straightforward process to show how software can improve model performance while <a href="https://youtu.be/60IJwbfjivs" rel="follow">achieving the goals of responsible, fair AI</a>&nbsp;
Fast Company&rsquo;s list honors projects that harness the power of data to empower change. The collaboration with TruEra is a compelling example of external data unlocking innovation to drive business growth. And this growth can be accelerated by <a href="https://www.demyst.com/platform?utm_marketing_source=BPST&amp;gaAd=FIXIT" rel="follow">the Demyst platform</a>, which eliminates the friction associated with external data procurement, testing, and deployment.

External data can be a key part of driving new and effective machine learning use cases.

Fast Company Recognizes How External Data Can Change the World

Paypal&rsquo;s recent discovery of <a href="https://www.finextra.com/newsarticle/39630/paypal-reveals-45-million-accounts-were-illegitimate-shares-plummet">4.5 million illegitimate accounts on its platform</a> should be a warning heeded by all: even the largest, most sophisticated payment processors can fall victim to bad actors.&nbsp;
Heightened customer expectations and persistent criminal attacks have made the situation near untenable. If legitimate transactions are denied, unhappy customers may take their business elsewhere. However, lenient approval processes leave companies vulnerable to <a href="https://demyst.com/blog/synthetic-identities-genuine-fraud/">synthetic fraud</a> and <a href="https://www.experian.com/blogs/insights/2020/12/solving-fraud-problem-first-party-fraud/">first-party fraud</a>.&nbsp;
These tensions have placed an increased emphasis on performing ID verification quickly and effectively. The process needs to be rapid enough to satisfy customer demands while remaining rigorous enough to protect against fraud.&nbsp;
An effective ID verification process considers 5 data points:&nbsp;
<ul>
<li style="font-weight: 400;" aria-level="1">A customer&rsquo;s name is their primary identifier, but it&rsquo;s also the easiest to steal. The question is whether the name can be matched with other known facts to confirm a valid identity.&nbsp; </li>
<li style="font-weight: 400;" aria-level="1">Although a customer&rsquo;s physical address is an important data point, it can change frequently. More than 10% of all US address records <a href="https://demyst.com/blog/how-to-verify-a-us-persons-address/">need to be updated each year</a>.&nbsp; </li>
<li aria-level="1">The phone number associated with a transaction is another data point that can change often &mdash; in the United States, approximately 100,000 mobile phone numbers are <a href="https://medium.com/codex/two-thirds-of-mobile-phone-numbers-are-reused-7fbf03e1b88f">reassigned each day</a>.&nbsp; </li>
<li aria-level="1">The email address of a customer provides additional details for determining whether a customer request is legitimate. It can be checked to see whether it was established recently and whether it matches known email addresses. </li>
<li style="font-weight: 400;" aria-level="1">An IP address also offers important clues about the validity of a transaction. If the IP address is from a region that is nowhere near a customer&rsquo;s known address, then the likelihood of fraud is much higher.&nbsp;</li>
</ul>
These details need to be verified against sources of truth that are accurate and up-to-date, accommodating the customer&rsquo;s desire for convenience while protecting the business against fraud.
Milena Babayev, Sr. Product Marketing Manager at Ekata, a Mastercard company, says &ldquo;Everyone wants to better leverage data insights &mdash; and your own, internal data, may not always be enough to predict fraud in real-time. The right identity verification solution is able to help you answer the right questions, such as when was the email first seen in digital interaction? Or is the IP address risky? Or are there any anomalies in usage of the identity elements? This is where Ekata&rsquo;s digital identity verification comes in &mdash; helping businesses reduce friction and improve fraud risk analysis &mdash; resulting in a better customer experience.&rdquo;
Companies can also strengthen their defenses by consulting multiple data providers for improved accuracy. Demyst&rsquo;s <a href="https://demyst.com/app/api-catalog/53">recipe for assessing fraud risk</a> applies sophisticated <a href="https://demyst.com/blog/demysts-entity-resolution-multi-source-recipes/">entity resolution technology</a> to conduct thorough examinations of individual details, including IP addresses.
The Demyst platform also benefits from Ekata&rsquo;s insights in other workflows, including <a href="https://demyst.com/app/api-catalog/64">a recipe for bad lead filtering</a>, which can improve marketing effectiveness and make outreach efforts more efficient. 
<a href="https://demyst.com/platform">Create an account and explore hundreds of operationalized external data sources ready for testing and deployment.</a>

Even sophisticated payment processors can fall victim to bad actors. An effective ID verification process considers 5 data points.

5 Checks for Flagging Fraud Risk

Even the largest, most sophisticated payment processors can fall victim to bad actors.

Market forces are changing the way that businesses collect, process, and apply external data. Mark Hookey, CEO of Demyst, has seen a major shift driven by data's widespread democratization.
&ldquo;There has never been a more interesting time to be in the data space,&rdquo; Hookey said. He shared his perspective as part of the Demyst webinar series for <a href="https://demyst.com/webinars/video?url=ZOHrC5NTp67NwEwqXNju&amp;gaAd=BPST">2022 data predictions</a>.
The automated cleaning and collection of data has lowered barriers and made it more accessible to a larger audience. &ldquo;There's a certain tipping point of the technology that is helping people claim data, which in turn will significantly boost the value people can extract from external data for their customers,&rdquo; Hookey explained.
Overall, this is good news that will lead to better business outcomes. Enterprises no longer need special relationships, access, or privileges to create a rich data ecosystem.&nbsp;
However, these changes have also shifted the dynamics that govern markets for external data. Massively increased access has meant that data providers are facing greater competition and lower profit margins for individual data products. Overall, Hookey expects this to increase revenues for data providers, because companies will be using more data.&nbsp;&nbsp;
&ldquo;Look to Netflix and the way that changed the dynamics for the movie studios,&rdquo; Hookey said. &ldquo;Obviously, in the short term, it caused a lot of flux and some real challenges.&rdquo; Over a longer period of time, consumers were given more options for streaming video, and <a href="https://www.forbes.com/sites/bradadgate/2021/06/17/nielsen-streaming-video-audience-share-is-higher-than-broadcast-tv/">they now spend more time watching it</a>.
&ldquo;It's the same in the world of data,&rdquo; Hookey said. &ldquo;People are consuming a lot more than they used to, and that's good for everyone.&rdquo;
Incumbents that have dominated external data markets can no longer rely on captive audiences and high switching costs; consumers are now using data exchanges like the <a href="https://www.snowflake.com/data-marketplace/">Snowflake Data Marketplace</a>, the <a href="https://aws.amazon.com/data-exchange/">AWS Data Exchange</a>, and <a href="https://demyst.com/platform?utm_marketing_source=BPST&amp;gaAd=BPST" rel="follow">the Demyst platform</a>, where they can leverage the unique strengths of multiple datasets.&nbsp;
Clients have become more sophisticated, <a href="https://demyst.com/blog/game-changer-demysts-entity-resolution-tech/">combining data from multiple providers</a> for clearer business insights. One example is the way that financial institutions are managing fraud risk.&nbsp;
&ldquo;People used to just talk about fraud,&rdquo; Hookey said. &ldquo;And now they talk about getting into the weeds of all of the specific, different types of fraud.&rdquo;&nbsp;
When data products specialize in fighting specific types of fraud &mdash; like <a href="https://demyst.com/blog/synthetic-identities-genuine-fraud/">synthetic fraud</a> or <a href="https://www.experian.com/blogs/insights/2020/12/solving-fraud-problem-first-party-fraud/">first-party fraud</a> &mdash; they can be <a href="https://fintechbusinessweekly.substack.com/p/fighting-fraud-with-sentilinks-naftali">less effective at fighting other types of fraud</a>. The democratization of data means that financial institutions can apply multiple products simultaneously, delivering the best results for their specific needs.&nbsp;
External data&rsquo;s tipping point will deliver a larger selection and lower costs for consumers, while data providers will see greater volumes of transactions and enough competition to keep them from getting complacent.&nbsp;
According to Hookey, &ldquo;It depends on the data source, and it depends on the quality, and that means it's a marketplace where the value goes more to the people that have high quality products.&rdquo;
View <a href="https://demyst.com/webinars/video?url=ZOHrC5NTp67NwEwqXNju&amp;gaAd=BPST">the full recording of this presentation</a> on Demyst&rsquo;s website.

External data’s democratization means more sophisticated clients and more developed marketplaces.

Blog

Finding Solutions for Data Bias

Using Snowflake for External Data Deployment

5 Hurdles Facing New Financial Products

The What, Why, and How of External Data

Fast Company Recognizes How External Data Can Change the World

5 Checks for Flagging Fraud Risk

External Data’s Tipping Point

Blog

Finding Solutions for Data Bias

Using Snowflake for External Data Deployment

5 Hurdles Facing New Financial Products

The What, Why, and How of External Data

Fast Company Recognizes How External Data Can Change the World

5 Checks for Flagging Fraud Risk

External Data’s Tipping Point

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