Uncover the critical role of data orchestration, AI-powered risk decisioning, and Provenir’s approach to combating fraud while maintaining a seamless user experience.
Can you provide an overview of the challenges and opportunities that financial institutions face in regard to fraud?
Per our recent survey of senior decision makers at financial services providers and fintechs 43 percent say identifying fraud is a top challenge, yet only 7 percent are completely confident that their anti-fraud measures are completely effective. This is concerning at a time when financial institutions are facing increasing financial fraud. A TransUnion study found the percentage of suspected global digital fraud attempts in financial services has increased significantly in recent years.
While fraud prevention is a high priority for financial institutions (FIs), it comes with a host of challenges. Those include:
● High False Positive Rates: False positives, mainly triggered by inaccurate models in fraud detection systems, can misidentify genuine applications as potentially fraudulent. Siloed data and limited visibility into customer activities across different channels also contribute to an increased number of false positives. This not only causes inconvenience for customers, but also results in unnecessary operational expenses.
● Increased Fraud and Changing Threats: Fraudsters constantly evolve their tactics, leveraging new technologies and methods. Traditional fraud detection systems may struggle to keep up with emerging threats.
● Regulatory Compliance: Financial institutions must comply with regulations and standards related to fraud and financial crime detection and prevention. Meeting these requirements while maintaining an efficient and customer-friendly process is a significant challenge.
● Vendor Reliance: Continuous refinement of fraud detection processes is key to mitigating emerging threats and new fraud patterns. FIs often struggle to be agile and update their models and data usage without extensive IT and/or vendor reliance.
As a result, we are seeing more and more FIs moving away from traditional fraud detection methods. FIs are now implementing a strategy of deploying a holistic, end-to-end risk decisioning solution that integrates fraud and risk management. This approach enables a more comprehensive view of their customers and their creditworthiness while more accurately detecting fraud by eliminating the siloed environment between the fraud and risk teams.
How has your professional journey and experiences shaped your perspective on the critical role of data orchestration in addressing the challenges posed by fraudsters in the financial services sector?
When looking back over the past 20 years of my career, there are some commonalities to approaches in fraud detection. However, one aspect that has not remained constant is the changing data landscape that is required to navigate and take advantage of, to best fight fraud. Several decades ago, an organization used its own data about its own customers to build profiles and note patterns, but they were often blind to the outside. In some jurisdictions there may have been blacklists or equivalent trying to amalgamate fraud characters, however these were variable and not so well regulated as default.
Today there is a rich, multi-layered landscape of data sources that can be leveraged to have the best chance at optimized fraud detection and proactively spotting the worst perpetrators. For example, a 360-degree view of a customer and potential fraudster can be built with powerful data orchestration bringing together identity data, mobile information, e-mail intelligence, biometrics and other key sources. This presents a great opportunity for organizations to have more sophisticated and high value fraud detection. So, with a wealth of data available, the orchestration of that data in a timely and efficient manner as well as the evolution of what data is used and the ability to plug and play with different integrations very quickly, is extremely pivotal.
In your role, what aspects of the fight against application fraud do you find most personally rewarding, and how does Provenir contribute to your vision in this domain?
Artificial intelligence (AI) continues to play an important role in identifying and predicting fraud attempts. As financial fraud and risk vectors are constantly evolving, accessing real-time data and applying it to the latest defensive measures in a fully automated fashion makes AI ideally fit for the fight.
Provenir has developed an approach which leverages optimized contextual scorecards, machine learning algorithms and outlier detection – all AI-infused strategies to improve fraud detection and accuracy. Deploying these strategies enables organizations to transition from traditional policy-based approaches to those that leverage predictive, explainable and scalable machine learning algorithms to radically improve the speed and accuracy of fraud decisioning.
In the end, the most rewarding thing is helping organizations transition from being reactive to fraud detection to a more proactive approach and mitigating losses in the first place. It has been very satisfying to see how organizations have evolved and for them to understand that advanced accurate fraud detection can, in fact, be a growth enabler for their business. If you really understand who you’re doing business with and their fraud risk, it means you can disrupt them very quickly if that risk is high. Or for the majority, you can expedite an experience for them and make it friction-free.
Provenir specializes in end-to-end risk decisioning solutions. How does Provenir’s approach stand out in integrating fraud and risk management to create a comprehensive defense against fraud attempts?
Provenir’s AI-Powered Risk Decisioning Platform enables organizations to stay ahead of fraud threats, with readily available data sources and easy integration for a more holistic view of their customers from all angles. This allows FIs to build the most comprehensive view of their customers by orchestrating the right data at the right time and allow AI model creation and monitoring to continuously optimize their fraud risk models, with configurable rules for faster responses as new threats arise.
Our approach incorporates three key tenets:
● Dynamic Data Orchestration: Seamless on-demand data orchestration of siloed fraud capabilities and data sources, leveraging pre-built integrations with over 120 relevant global data providers. This results in a single view of the customer to maximize the identification of patterns indicative of fraud.
● Smarter Fraud Detection: Iteratively develop, monitor, and enhance AI models for more accurate fraud decisions and intelligent data use, while regularly updating detection algorithms to address emerging threats.
● Optimized Fraud Investigations: Investigate fraud alerts and manage cases with a deep-dive user interface.
Many organizations look at risk holistically in that there is often correlation between fraud risk, credit risk and compliance risk. A key strength of Provenir is that we address that risk holistically on a single platform, eliminating siloed environments between a customer’s credit, identity, and fraud risk teams.
Could you elaborate on how Provenir’s data orchestration solutions specifically cater to the need for a holistic view of customers, aiding in more accurate fraud detection and prevention?
A more holistic, integrated view of a financial institution’s customers enables the organization to stay ahead of threats, and Provenir’s AI-Powered Risk Decisioning Platform ensures it can continually improve fraud risk models and optimize decisions as threats evolve – all right alongside credit risk decisions. Eliminating these siloed environments offers maximum flexibility and agility at every step of an organization’s risk-decisioning processes. This reduces the complexity of managing multiple online fraud detection tools and disparate decisioning systems by having one unified, end-to-end solution for fraud, credit, and compliance across the customer journey.
Considering the surge in fraud attempts, how does Provenir collaborate with financial institutions and other partners to ensure the continuous evolution of fraud prevention strategies?
A key component of Provenir’s AI-Powered Risk Decisioning Platform is Provenir Data. Provenir Data is a global data and intelligence platform that makes accessing data fast and easy for our customers.
Through a single API, Provenir Data brings together a curated range of data and data solutions known as the Provenir Marketplace. The Provenir Marketplace, which includes more than 100 data partners, is a comprehensive fintech data and intelligence ecosystem covering the whole customer lifecycle with data types such as identification, AML, document verification, open banking, PEPs/sanctions, bureau data, mobile data, email data, device verification, facial biometrics, and social media validation. This data enables businesses to make smarter decisions across key areas such as fraud, KYC, origination, credit risk, and financial inclusion.
What role does collaboration and information sharing play in Provenir’s strategy to combat identity fraud, especially in a landscape where fraudsters are constantly adapting their methods?
As fraud threats evolve, the question becomes how can financial services organizations keep up? A key consideration is how an FI orchestrates and integrates data; as mentioned previously, there is no shortage of fraud data providers available, but the real challenge is how to use the data. This is where our strategy plays a key role. First, Provenir’s AI-Powered Risk Decisioning Platform enables FIs to optimize fraud decisions based on their specific data needs, with an extensive variety of readily available data sources and easy integration into decisioning workflows.
FIs can now evolve their risk models as quickly as the fraud threats targeting the industry. Deploying Provenir’s AI-Powered Risk Decisioning Platform ensures continuous improvement in fraud risk models with AI model creation and monitoring, testing, and optimization.
At Provenir, we work closely with our customers all over the world and collaborate with them to better understand their fraud risk concerns. As a result, organizations can reduce the complexity of managing multiple online fraud detection tools and disparate decisioning systems with an end-to-end solution for fraud, credit, and compliance across the customer journey.
Can you provide insights into the technological advancements or innovations that Provenir is incorporating to stay ahead of the curve in preventing application fraud?
Provenir incorporates several technological advancements that help our customers address fraud risk. These include:
● Low code enhancements for users, to pivot and evolve their fraud analytics with speed.
● Ongoing centralization of data integrations to try new fraud data sources with ease.
● Real-time decisioning observability to learn and spot fraud trends easily.
● Reduce burden of retraining and model governance.
● Improved model monitoring for wider set of models.
● Ongoing additions to the case management module to enhance fraud Investigation.
How does Provenir strike a balance between offering robust fraud prevention solutions and ensuring a seamless and efficient experience for its clients and their end-users?
Provenir actually helps its customers on their journey of balancing fraud detection solutions with the end-user experience. We ensure the fraud detection is accurate and fast, enabling our customers to better serve their applicants when addressing fraud threats.
Looking forward, what strategic initiatives or developments can we expect from Provenir in the realm of data orchestration, and how do these align with the broader industry trends and challenges?
At Provenir, it’s about making sure our data orchestration continues to be fast and efficient, and increasingly important, scalability for our customers. Secondly, it’s about continuing to build on our partnership data ecosystem to serve our customers with ready-made integrations to the right data needed to fight a specific problem, such as fraud.
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