According to a National Academy of Medicine discussion paper, social determinants of health (SDoH) account for upwards of 80% of a population’s health outcomes. Breaking this down further, 50% come just from socioeconomic and physical environment factors such as education, employment, income, family and social support, community safety, air and water quality, and access to housing and transit. SDoH determine access to and quality of healthcare, and are contributors to a system of disparate access to care.

We believe that to more efficiently provide comprehensive healthcare access to more people, providers will leverage technology to blend in-person and virtual modes of care delivery.

In this article, Amwell and Google Cloud examine five ways telehealth – as part of a provider’s overall care model – can help democratize access to healthcare. (graphic url) 

Remove distance as a barrier to care

8.6 million Americans live more than 30 minutes from their nearest hospital. Long drives can deter patients from seeking care or maintaining routine visits. On the flip side, 92% of Americans nationwide have access to wired broadband in the home or through mobile broadband. Therefore, having a virtual visit just a click away can help remove barriers to care, like distance.

Eliminate the risk of unnecessary exposure

Virtual care means that patients don’t have to worry about potential exposure in transit, while sitting in waiting rooms, or from direct interactions during in-person health visits. This is particularly relevant when it comes to those with chronic diseases or underlying conditions. Telehealth provides patients who may be more susceptible to diseases with access to continuous healthcare without putting them at higher risk for developing more severe symptoms. Virtual visits can occur in the comfort –and safety– of patients’ homes.

Extend access to specialized care

55% of preventable hospitalization or mortality in rural settings is due to lack of access to specialty care. With telehealth, physical proximity to specialized services–typically in urban areas–can be reduced as a limiting factor. Virtual solutions grant everyone access to top specialists, regardless of location.

Save time and money

By augmenting in-person visits with telehealth applications, providers can benefit from greater efficiencies in scheduling, helping to improve their bottom line, and add more flexibility to their workday. Meanwhile, patients can spend less on travel and childcare, limit time taken off work, and save on other costs associated with in-person visits — for a savings of $35 to $690 per visit. In a survey by the COVID-19 Healthcare Coalition, 76% of the 2,000+ patients surveyed across the US responded that transportation was removed as a barrier, 65% reported they no longer had to take time off from work for an appointment, and 67 percent reporting lower costs than an in-person visit.

Combat physician shortage and fatigue

Research shows there will be a shortage of more than 100,000 doctors by 2030. In the midst of a physician shortage, telehealth can help improve care delivery and make it more efficient, ensuring more people can still have their healthcare needs addressed. Additionally, by integrating intelligence such as case triaging along with telehealth into a virtual care model, providers can help reduce clinician burnout.

Amwell and Google Cloud are partnering to deliver transformative telehealth solutions that will make it easier for more patients to receive care and improve patient and clinician experiences across the continuum of care. One example includes embedding real-time captioning and translation services powered by Google Cloud’s AI and NLP technologies within the Amwell platform to increase health access and understanding for more people.

As physicians, we are excited that the future of healthcare will continue to blend cloud technologies like EHR-integrated telehealth platforms, AI, healthcare-trained virtual agents along with in-person care to create an integrated hybrid care model that will improve patient outcomes and unburden providers, all while expanding access to broader patient populations.

To learn more, download the whitepaper “Healthcare’s Virtual Transformation,” written in conjunction with Becker’s Hospital Review.

^{1: “Social Determinants of Health 101 for Health Care: Five Plus Five,” National Academy of Medicine, October 2017}

API security incidents are increasingly common and disruptive. With the growth of API traffic, enterprises across the world are also experiencing an uptick in malicious API attacks, making API security a heightened priority. According to our latest API Security Research Report, 50% of organizations surveyed have  experienced an API security incident in the past 12 months and of those, 77% delayed the rollout of a new service or application. 

At the RSA Conference 2023 today, we’re making it faster and easier to help detect API abuse incidents with the introduction of Advanced API Security Machine Learning powered abuse-detection dashboards. Our newly introduced Machine Learning models are trained to detect business logic attacks. 

These types of attacks are notoriously hard to identify, and target APIs tied to intellectual property, business processes, or sensitive information, such as user data, listing of goods, or crediting accounts. These APIs must be accessible to provide business value, but have also become targets for attackers.

API security incidents can have a considerable impact on an organization’s operations and its bottom line. In June 2022, Imperva released a report titled Quantifying the Cost of API Insecurity, which estimates that lack of secure APIs could result in an average annual API-related total global cyber loss of between $41 billion to $75 billion annually. Furthermore, according to IBM’s  2022 Cost of a Data Breach Report, the average cost of a data breach is $4.35 million. It’s vital that organizations detect and mitigate API abuse incidents early to prevent prolonged fiscal and reputational damage to the business.

However, business logic attacks are harder to detect using static security policies, which allows attackers to manipulate legitimate functionality to achieve a malicious goal without triggering any static security alerts. For example, if a malicious actor gains control of a server and makes subtle changes, the shift in activity patterns of the server is generally undetectable to most monitoring tools. However, in this scenario, the Advanced API Security’s ML-powered API abuse detection model can help differentiate between legitimate and deviant traffic and immediately notify key stakeholders to act quickly and minimize blast radius of the problem.

The ML models that power API abuse detection have been trained and used by Google’s internal teams to help protect our public-facing APIs. The models rely on years of learning and best practices and are now available to all Apigee Advanced API Security customers.

Another challenge in detecting API abuse incidents is the volume of alerts. To reduce the risk of missing key security incidents, many static rules that detect less sophisticated attacks are incredibly sensitive: They generate a high volume of alerts. This makes finding the critical incidents within API traffic and acting to resolve them like “finding a needle in a haystack” for many IT teams. Apigee Advanced Security’s ML-powered dashboards more accurately identify critical API abuses and find similar patterns within the large number of bot alerts to help reduce the time to find and act on most important incidents.

With the help of Apigee Advanced API Security’s ML-powered abuse detection dashboards, customers can uncover critical API abuse incidents, including business logic attacks, scraping, and anomalies, faster. Critical threats are surfaced with clear and concise descriptions to capture the essence of the attack along with the most important characteristics such as the source of the attack, the number of API calls, and the duration of the attack, to help resolve the incident more rapidly. 

Machine Learning powered abuse-detection dashboards are available in Advanced API Security, a feature of Apigee API management that enables you to more easily detect API security misconfigurations, bad bots, and malicious activities. 

To get started with Advanced API Security’s ML-powered dashboards, start your free Apigee trial now.

Advances in artificial intelligence (AI) and machine learning (ML) have led to increased adoption in the financial services sector. A prominent use for this technology is to assist in key compliance and risk functions, including the detection of fraud, money laundering, and other financial crimes and illicit finance, as well as trade manipulation — collectively referred to as “Risk AI/ML.” As the use of these models grows, so do questions about managing risks associated with the models. 

In particular, regulators, financial institutions, and technology service providers have been looking into whether existing Model Risk Management (MRM) guidance — which has traditionally been the regulatory regime applicable to managing model risk in the financial services industry — continues to be relevant for AI/ML models. And, if so, how should the guidance be interpreted and applied to this new technology?

As the financial sector increasingly adopts artificial intelligence and machine learning techniques, it is critical for regulators, financial companies and technology providers to work together to assure that there are clear rules of the road,” says Jo Ann Barefoot, AIR CEO and co-founder. “Updated guidelines on the responsible use of these models can help prevent novel technologies from causing harm, and can also open up better ways to combat risk in areas like money laundering, illicit finance, and fraud.

Our new white paper, written in partnership with the Alliance for Innovative Regulation (AIR), seeks to address that question, with the aim of fostering thought and dialogue among agencies, the financial services industry, risk model vendors, and entities interested in the performance, outputs, and compliance of models used to identify, mitigate, and combat risks in financial services. This white paper does not address issues that may arise with other applications of AI/ML in the financial services industry, such as consumer credit underwriting or models using generative AI or Large Language Models, which are better addressed iteratively. 

The paper argues that MRM guidance, given its broad, principles-based approach, continues to provide an appropriate framework for assessing financial institutions’ management of model risk, even for Risk AI/ML models. Working within an existing framework takes advantage of the knowledge and operational capabilities of institutions that already understand this framework, instead of having to create an entirely new approach, which generally takes longer to implement and make effective. Nonetheless, the paper recognizes that AI/ML models have unique traits and characteristics compared to conventional models, including their potential dynamism and pattern recognition capabilities. These distinctions must be in focus when considering how MRM guidance should be applied to Risk AI/ML models. 

Taking into account those unique aspects of AI/ML models, the paper offers specific observations and recommendations regarding the application of MRM guidance to Risk AI/ML models, including:

• Risk assessment: In assessing risk, it is important to recognize that AI/ML models are not inherently more risky than conventional models. A risk-tiering assessment must consider the targeted business application or process for which a model is used, as well as the model’s complexity and materiality. To assist in these assessments, regulators could clarify that the use of AI/ML alone does not place a model into a high-risk tier and publish further guidance to help set expectations regarding the materiality/risk ratings of AI/ML models as applied to common use cases.
• Safety and soundness: Due to the dynamic nature of Risk AI/ML models, reliance on extensive and ongoing testing focused on outcomes throughout the development and implementation stages of such models should be primary in satisfying regulatory expectations of soundness. To that end, the development of technical metrics and related testing benchmarks should be encouraged. Model “explainability,” while useful for purposes of understanding the specific outputs of AI/ML models, may be less effective or insufficient for establishing whether the model as a whole is sound and fit for purpose.
• Model documentation: The touchstone for the sufficiency of documentation should be what is needed for the bank to use and validate the model, and understand its design, theory, and logic. Disclosure of proprietary details, such as model code, is unnecessary and unhelpful in verifying the sufficiency of a model and would deter model builders from sharing best-in-class technology with financial institutions.
• Industry standards and best practices: Regulators should support the development of global standards and their use across the financial services and regulatory landscape by explicitly recognizing such standards as presumptive evidence of compliance with the MRM guidance and sound AI/ML risk mitigation practices. In addition, regulators should foster industry collaboration and training based on such standards.

Governance controls: Regulators should use guidance to advance the use of governance controls, including incremental rollouts and circuit breakers, as essential tools in mitigating risks associated with Risk AI/ML models.

In an era where AI technology has the potential to revolutionize financial services, we acknowledge the foresight of our regulators in setting a solid foundation and blueprint for navigating the labyrinth of potential risks through the MRM guidance,” says Philip Moyer, Global VP, AI and Business Solutions at Google Cloud. “We believe there is room for greater coherence and precision, enhanced risk-mitigation approaches, and refined best practices surrounding AI and ML risk models. Whether it’s in capacity building or information sharing, our call to action is for greater collaboration between regulators and financial institutions. We’re confident that our collective efforts today will help shape a more robust and resilient future for financial services.

We invite a discussion of additional considerations, including the importance of examiner and industry training and collaboration, as well as openness by regulators to continue to refine the MRM guidance as AI/ML technologies develop and standards emerge. 

Implementing our recommendations would advance several goals. It would help regulators, financial institutions, and technology providers work together to better serve their shared purpose of protecting the safety and soundness of the financial system. At the same time, implementing the recommendations and continuing work in this space would promote the adoption of cutting-edge technologies in the industry, including those that combat such scourges as money laundering, illicit finance, and fraud.

You can read the full white paper here.

If you’ve opened an account at a bank, applied for a government benefit, or provided a proof of age document on an ecommerce website, chances are you’ve had to share a physical or digital copy of a Driver’s License or a passport as proof of your identity. For businesses or public sector organizations that need this information to provide services, processing images of identity documents has long been a time- and resource-intensive process that requires extensive human intervention. Solutions exist to help digitally capture the data, but they require extensive human intervention that impacts the speed and cost of processing and ultimately the time to service customers.

The Google Cloud Document AI family of solutions has been designed to help solve some of the hardest problems for data capture at scale by extracting structured data from unstructured documents to help reduce processing costs and improve business speed and efficiency. Today, we’re announcing the general availability of identity parsers that bring the power of Document AI to customer verification, KYC, and other identity-based workflows.

With Document AI for Identity, businesses can leverage automation to extract information from identity documents with a high degree of accuracy, without having to bear the cost and turnaround time of manual tasks by a service provider. Document AI for Identity leverages artificial Intelligence to provide a set of pre-trained models that can parse identity and supports US driver’s licenses (generally available), US passports (generally available), French driver’s licenses (preview) and French National ID cards (preview), with more documents to be added from around the world over the coming months.

When our customers process high-volume workloads or complex workflows, they need a high degree of accuracy, since getting the first step wrong can derail the entire workflow. The introduction of special parsers for Identity processing can help solve one of the most commonly required document processing needs that our financial services and public sector customers face.

Along with the identity parsers, Google Cloud is also offering its “Human in the Loop” service, in which verification for a subset of identity documents can be automatically assigned to a pool of humans (internal or external) for manual review, based on confidence scores.

While there are multiple industries and applications that could benefit from Document AI for Identity, we’ve seen two main kinds of applications being adopted during the solution’s preview. One is around processing ID cards uploaded as unstructured images at scale, so that enterprises can have IDs on file. The second use case is to perform advanced checks on identity documents to validate their authenticity and / or to detect fraud. Google Cloud’s fraud detector API (which is currently in preview) can complement Document AI for Identity and apply an extra layer of normalization to help validate the identity as a government-issued ID by checking for suspicious words, image manipulation, and other common issues with forged identity documents. With new versions of driver’s licenses being frequently released, Document AI for identity uses specialized models and constantly-updated training data to help make sure the parsers can offer a high degree of accuracy. For all use cases, Document AI does not retain any customer data after completing the processing request (successfully or with an error).

Check out this demo and visit the Document AI for Identity landing page for more information on how Document AI can help solve your identity processing needs, and ask your Google Cloud account team to help you integrate Identity Document AI into your workflows.

For practitioners who’re interested in trying out Identity DocAI, check out our companion practitioner blog for step by step instructions on how to get started.