Allied is one of the largest providers of insurance, lending, risk management, and data-driven solutions to financial institutions in North America. With heightened regulatory scrutiny on finance and insurance (F&I) compliance, lenders, dealerships, and business process outsourcing (BPO) organizations face intensified pressure to meet stringent regulatory requirements.

“This collaboration is evidence of just how deeply PCMI and Allied understand the demand for a comprehensive lender compliance solution,” said Pete Hilger, CEO at Allied. “Although the FTC CARS Rule is facing some challenges, lenders are already feeling the pressure in maintaining compliance with GAP policy refunds. PCMI’s TruComply will provide the necessary tools to ensure that lenders and lender BPO services are better positioned to mitigate litigious situations.”

TruComply is poised to revolutionize lender compliance by streamlining the F&I product cancellation and refund process while ensuring compliance for lenders. TruComply automates refund quotes with access to the largest provider network and delivers seamless payment distributions with compliance verification.

“TruComply was carefully designed to meet the evolving regulatory lender compliance requirements,” said Mark Nagelvoort, CEO at PCMI. “We see TruComply providing a modern experience for managing policy refunds, compliance verification, and for ensuring industry compliance on behalf of lenders, dealers, and providers.”

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intelliflo redblack customers can now effortlessly define and implement compliance rules tailored to each client, automating compliance checks during rebalancing and trading. No more hours lost to time-consuming manual compliance verification. With intelliflo redblack, advisors can now focus on what matters most – serving their clients and growing their businesses – while also gaining complete confidence that client mandates are not accidentally violated.

“Our mission at intelliflo has always been to empower advisors with the tools they need to succeed,” says Jennifer Valdez, President, Americas for intelliflo. “With intelliflo redblack’s compliance rules engine, we’re taking that mission to the next level. We’re giving RIAs all sizes the power to efficiently implement compliance like never before, reducing risk, saving time, and ultimately driving success.”

“Manually customizing compliance rules across individual clients is a time-consuming, tedious task for advisors,” said Alois Pirker, Founder and CEO of Pirker Partners. “intelliflo redblack’s new compliance rules engine allows RIAs to effectively apply personalized compliance rules without having to spend dozens of hours ensuring trades adhere to complex custom rules for each portfolio. This is functionality that has previously only been available at the institutional level, but now is accessible to all investment advisors.”

intelliflo redblack has been on a journey of continuous innovation. Over the last year, intelliflo redblack has also introduced advanced tax management capabilities, cash and wash sale management options and a complete overhaul of the order management system. Over 25% of the top 50 largest RIAs in the U.S. by AUM currently leverage intelliflo redblack.

About intelliflo

intelliflo widens access to financial advice through leading technology powering the financial advisory experience. We use open software architectures combined with unmatched industry experience to simplify a complex digital landscape to help advisors compete and grow. Our solutions support over 30,000 financial advisors worldwide, representing over three million end-investors, with over $1 trillion serviced across our platforms.

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Money laundering is a complex problem with a growing global impact. The amount of money laundered each year is estimated to be 2-5% of global GDP, or up to $2 trillion annually.¹ Money laundering proceeds are connected to illegal activities, ranging from drug and human trafficking to terrorist financing. Today, anti-money laundering programs consume significant resources for financial institutions, many of which operate across a variety of global and regional regulatory bodies. In fact, large financial institutions report monitoring four billion² transactions or more a year for increasingly sophisticated illicit behavior.

Most legacy AML monitoring products are reliant on manually defined rules, which yield low rates of identifying suspicious activities. Even in the most advanced implementations of rules-based systems, money launderers can learn and work around these rules to avoid detection. In fact, more than 95% of system-generated alerts turn out to be “false positives” in the first phase of review, with approximately 98% never culminating in a suspicious activity report (SAR).³ High rates of false positives require manual reviews, which costs the industry billions of dollars in wasted investigation time each year and distract institutions from true suspicious activity.⁴

AML AI helps global financial institutions increase risk detection and lower operational cost

Google Cloud’s AML AI provides a consolidated machine learning (ML)-generated customer risk score as an alternative to rules-based transaction alerting. The risk score is based on the bank’s data including transactional patterns, network behavior, and Know Your Customer (KYC) data to identify instances and groups of high-risk retail and commercial customers. The product can adapt to changes in underlying data, delivering more accurate results, which increases overall program effectiveness and improves operational efficiency.

Google Cloud’s AML AI is using proprietary ML technology as well as Google Cloud technologies, such as Vertex AI and BigQuery. The product handles the complexities of running ML at scale, while also providing enriched explanations of the outputs to enable financial institutions to expedite the investigation workflow and improve the customer experience. To date, the solution has been put in production across several geographical regulatory jurisdictions.

“Google is a pioneer in AI, and now we’re making our tools, technologies, and expertise available to solve one of the biggest and most costly challenges in the financial services industry,” said Thomas Kurian, CEO of Google Cloud. “Building on our commitment to bring AI-powered innovation to the financial services industry, we are launching Google Cloud’s AML AI to help financial institutions more accurately and efficiently identify AML risk while enhancing business operations and governance.”

Google Cloud’s AML AI product delivers the following benefits:

• Increased risk detection: AML AI can outperform current systems in detecting financial crime risk. Google Cloud customer HSBC found that they can now detect two to four times^[5] more true positive risk, enhancing their ability to identify and prevent money laundering activities.
• Lower operational costs: AML AI minimizes wasted investigator time by reducing alert volumes and providing explainable outputs that speed up individual investigations. In fact, HSBC saw alert volumes decrease by more than 60%.
• Improved governance and defensibility: AML AI provides financial institutions with auditable and explainable outputs to support internal risk management. This approach is now in production in several geographies, each with their own regulatory requirements.
• Improved customer experience: By increasing precision and significantly reducing false positives, AML AI minimizes the need to engage with customers for additional compliance verification checks.

HSBC, Bradesco, and Lunar find significant value in an AI-based approach to AML 

Using Google Cloud’s AML AI as its core, HSBC adopted a cloud-based AI-first approach as its primary AML transaction monitoring system in its key markets. Google Cloud’s AML AI helped HSBC improve detection capability, deliver more accurate results, and significantly reduce batch processing times for its large customer base. As a result, HSBC was awarded the Celent Model Risk Manager of the Year 2023.

“Google Cloud’s AML AI has significantly improved HSBC’s AML detection capability. Google’s models are already demonstrating the tremendous potential of machine learning to transform anti-financial crime efforts in the industry at large,” said Jennifer Calvery, Group Head of Financial Crime Risk and Compliance at HSBC. “By enhancing our customer monitoring framework with Google Cloud’s sophisticated AI-based product, we have been able to improve the precision of our financial crime detection and reduce alert volumes meaning less investigation time is spent chasing false leads. We have also reduced the processing time required to analyze billions of transactions across millions of accounts from several weeks to a few days.”

“As threats become more sophisticated globally and the challenges in fighting money laundering become increasingly complex, we believe in the combination of AI and decision science as the best strategy to detect suspicious activity with more accuracy and efficiency,” said Rafael Cavalcanti, SVP Data & Analytics, Bradesco. “As one of the largest banks in Brazil with more than 70 million customers, we see the value of Google Cloud’s AML AI product for the financial industry and have greatly enjoyed working with Google Cloud in advancing the industry’s approach to anti-money laundering.”

“Transforming the traditional AML approach with AI technology can help the financial industry keep pace with rapidly evolving money laundering techniques and the increasing volume of financial transactions,” said Jonas Leed, Group General Counsel & Money Laundering Reporting Officer (MLRO), Lunar. “As a digital bank, Lunar prides itself on embracing transformational technology that creates efficiencies so we can focus on delivering the best banking experience to our customers. We are encouraged and inspired by Google Cloud’s AML AI ability to more accurately detect money laundering.”

AML AI can help customers reduce their operational costs while simultaneously improving the strength of their AML program. In the future, Google Cloud plans to provide Generative AI foundations for the financial services industry with the goal of boosting employee productivity, for example, to reduce the time needed for an analyst to investigate potential suspicious activity.

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