Latest news with #frauddetection
Yahoo
2 days ago
- Business
- Yahoo
Unifying customer experience, risk management and fraud detection in financial services
In today's fast-paced financial landscape, institutions must strike a delicate balance between delivering a seamless customer experience, managing risk in real time, and preventing fraud before it occurs. However, these critical functions often operate in isolation, leading to inefficiencies, increased operational costs, and a fragmented user experience. The traditional siloed approach hinders the ability to detect threats quickly and compromises customer trust. To address these challenges, financial institutions must adopt a unified decision intelligence framework that integrates real-time customer analytics, risk and fraud strategies that are deployed as autonomous agents. Despite the growing adoption of artificial intelligence (AI) in the financial sector, many banks still deploy AI solutions in isolated areas like customer service chatbots, credit risk assessments, or fraud and financial crime monitoring. While these applications offer some benefits, they fall short of unlocking the full potential of Agentic AI. A more holistic approach, however, enables financial institutions to achieve real-time fraud detection, instant risk assessment, and seamless customer interactions within a fully governed and observable platform. By leveraging AI and machine learning, banks can analyse vast amounts of data across multiple touchpoints, identifying patterns and anomalies that signal fraud or heightened risk. This is supplemented through real-time integration with third-party risk information. This real-time capability allows organisations to maximise the use of intelligence to prevent fraudulent transactions, while simultaneously ensuring that legitimate customers enjoy a frictionless journey. A unified decisioning platform eliminates redundancies, enhances security, and ensures regulatory compliance, all while improving customer satisfaction. One of the most significant advantages of this approach is its ability to connect currently siloed processes into a seamless workflow. For example, a new customer applying for a loan would traditionally undergo multiple disconnected steps, such as Know Your Customer (KYC) verification, fraud checks, risk assessments, and the final loan decision. Each of these steps operates independently, often causing delays or friction. With a unified AI-driven approach, these processes can be integrated into a single workflow, where real-time AI models assess risk, verify identity, and detect fraud in parallel, allowing for instant decision-making. This means that instead of waiting days for approval, a customer can receive a loan decision within seconds. The result is an improved experience, enhanced regulatory compliance, and reduced financial risk for the institution. This transformation is made possible through intelligent automation and a cohesive decisioning strategy. A key advantage of unified decisioning is the ability to harness information collected in the moment, with historical customer data and further supplement that view with data points from third-party services such as bureau data or live risk monitoring. Traditional risk and fraud detection methods rely on manual processes and rule-based systems that often lag behind evolving threats. In contrast, AI-powered decisioning systems can process and interpret large datasets in milliseconds, enabling immediate action when suspicious activity is detected. Automation further enhances this approach by reducing human intervention in routine tasks. Commonly decision strategies are parameterised allowing analysts to apply low impact changes, which when combined with automated testing allows strategy changes to be operationalised in hours compared to the weeks financial organisations historically required. One example is Finland's top retail bank, S-Bank, which has leveraged advanced AI and machine learning technologies from SAS to streamline its credit scoring and enhance customer service. By automating credit risk processes, the bank has reduced loan processing times while maintaining accuracy. With real-time decision-making and AI-driven analytics, S-Bank has strengthened customer relationships and optimised business outcomes, demonstrating how AI-powered decisioning not only enhances operational efficiency but also fosters customer trust in a competitive market. This unified approach, bringing data, analytics, and decisioning together, has enabled S-Bank to act faster, reduce silos, and drive smarter, more connected outcomes across the business. Customer expectations have evolved over recent years, with modern consumers demanding personalised, instant, and secure financial services. To meet these expectations, it's crucial to address the challenges posed by fragmented fraud detection and risk assessment systems. A disjointed approach can lead to delays, unnecessary security checks, and false positives, all of which can frustrate customers. By implementing a unified AI-driven decisioning framework, financial services can seamlessly integrate fraud prevention measures, ensuring a smooth and secure experience that allows legitimate transactions to proceed without disruption. For example, AI-powered behavioural biometrics can assess a user's interactions in real time, such as typing speed and device usage, to verify authenticity without requiring additional verification steps. This proactive approach reduces friction, enhances security, and builds customer trust by ensuring that protective measures do not come at the expense of convenience. Agentic AI is rapidly emerging as a transformative force across industries, from personal productivity to enterprise execution. Agents are recognised for their ability to orchestrate deterministic and probabilistic AI, along with the logical based rules required to put decisions into effect. Crucially they are deployed to execute independently of the environment which designed them, and by using cloud native technology (containers) they can be tested and deployed automatically. Through cloud orchestration, agents can run at scale and between availability zones or regions to provide resilience and respect data sovereignty. In financial services, this advanced AI paradigm is playing a pivotal role in unifying decision-making processes, enabling institutions to dynamically assess risk, detect fraud, and enhance customer engagement with minimal human intervention. By leveraging Agentic AI memory, banks can create intelligent workflows that adapt to shifting data patterns, allowing business analysts to refine business rules, or automatically update AI models. This level of automation and intelligence is particularly impactful in areas such as credit underwriting, where AI-powered agents can continuously refine risk models based on new transaction data and fraud signals, ensuring that financial institutions stay ahead of evolving threats while delivering seamless customer experiences. The success of unified decisioning in financial services highlights its potential for widespread adoption across the industry. As AI technology continues to evolve, financial institutions must embrace a strategic approach that prioritises integration and scalability. Cloud-based AI platforms offer a cost-effective way to implement unified decisioning across multiple business functions, allowing banks to scale their operations while maintaining agility. With the cloud, banks can seamlessly integrate advanced AI capabilities, ensuring real-time data processing and decision-making across various departments, ultimately driving both efficiency and innovation. Regulatory compliance is another crucial factor in the adoption of AI-driven decisioning. Financial institutions must ensure that their AI models align with evolving regulatory frameworks and trustworthy AI principles. Transparent AI decision-making processes, explainable models, and robust audit trails are essential components of a responsible AI strategy that balances security, compliance, and customer experience. What is also important, within that transparent framework, is democratising access to the technology so a wide range of employees can build and deploy models – this can be achieved through low-code and no-code software that caters for employees with different levels of expertise. As the financial landscape grows increasingly complex, the need for a seamless, integrated approach to customer experience, risk management, and fraud detection has never been more critical. AI-driven decisioning presents a powerful solution, enabling banks to break down silos, optimise risk management, and deliver an enhanced customer experience at scale. Agents have the capacity to heighten customer interaction over the coming years – whether that's through agents that assist employees (e.g. in call centres) being able to automatically access the relevant information rather than search for it while the customer is left waiting, or agents that can self-serve customers directly. The path forward for financial institutions is clear: those that invest in AI-driven unified decisioning will not only enhance security and compliance but also redefine the banking experience for the digital age. With real-time analytics, automation, and intelligent risk management, the financial sector can achieve a future where fraud prevention and customer satisfaction go hand-in-hand. David Shannon is Head of Decisioning at SAS UK & Ireland "Unifying customer experience, risk management and fraud detection in financial services" was originally created and published by Retail Banker International, a GlobalData owned brand. The information on this site has been included in good faith for general informational purposes only. It is not intended to amount to advice on which you should rely, and we give no representation, warranty or guarantee, whether express or implied as to its accuracy or completeness. You must obtain professional or specialist advice before taking, or refraining from, any action on the basis of the content on our site. Sign in to access your portfolio
Associated Press
3 days ago
- Health
- Associated Press
Top Healthcare Experts To Review Anti-Fraud Strategies At Health 2.0 Conference
The Health 2.0 Conference will bring top industry leaders together to explore pressing healthcare fraud issues and share innovative solutions for prevention. LAS VEGAS, NV, UNITED STATES, July 4, 2025 / / -- As the healthcare sector continues its digital evolution, the rise of fraud-related challenges across medical, insurance, and telehealth domains has called for an urgent industry response. In light of this growing concern, the upcoming Health 2.0 Conference is poised to serve as a premier global platform for addressing fraud within the healthcare ecosystem. The three-day healthcare event is scheduled to take place from April 7–9, 2026, at the iconic Bellagio Hotel & Casino in Las Vegas, USA. Healthcare fraud including billing scams, data manipulation, unlicensed practitioners, and spam telehealth services has escalated dramatically in recent years. From undermining trust in digital health innovations to posing threats to patient safety and privacy, the ripple effects of fraudulent practices have become increasingly difficult to ignore. Recognizing the urgency of the situation, the Health 2.0 Conference aims to promote in-depth dialogue on regulatory frameworks, emerging fraud detection technologies, and shared accountability across the sector. Experts at the health conference will review how technology-driven fraud from AI-generated misinformation to phishing in telehealth platforms is compromising care delivery, insurance operations, and data confidentiality. These developments are particularly critical in light of the expanding role of virtual care and remote diagnostics, which often lack standardized oversight mechanisms. The event will convene top minds from healthcare institutions, government bodies, insurance firms, cybersecurity companies, and digital health startups to collectively analyze the vulnerabilities plaguing the sector. The agenda will feature keynote presentations, fireside chats, and research showcases focused on both the scale and subtlety of fraud tactics in today's interconnected healthcare infrastructure. 'We're witnessing a tectonic shift in how healthcare services are delivered and monetized. With this transformation, unfortunately, comes a wave of complex fraud schemes that exploit systemic gaps and consumer trust. Our goal through this event is to review those vulnerabilities and champion collaborative frameworks that protect stakeholders — patients, providers, and payers alike. Fraud isn't a peripheral issue anymore; it's a central threat to healthcare innovation, and this conference intends to address it head-on,' said Aayushi Kapil, Manager at Health 2.0 Conference. The Health 2.0 Conference's fraud-centric track will delve into topics such as fake drug approvals, cyber attacks on patient portals, impersonation fraud during virtual consultations, and misuse of AI tools in diagnostics and insurance claims. Attendees can expect in-depth panels that unpack high-profile scam cases and offer real-world insights from investigators, whistleblowers, and policy watchdogs. Beyond identification of fraud, the Health 2.0 Conference will offer proactive strategies such as how AI and machine learning can be deployed for anomaly detection, audit automation, and behavior-based red-flag systems. Case studies from leading health systems and digital platforms will demonstrate how transparent workflows and robust internal controls are helping to insulate operations from manipulation. Several sessions will also center on empowering consumers with education, highlighting the role of transparency in medical billing, and advocating for stronger vetting of healthtech platforms. Topics such as ethical AI development, algorithmic bias, and the dangers of medical misinformation will round out the holistic approach to trust-building in health services. Additional discussions will explore the ethics of data sharing in research, the growing importance of user consent in wearable health devices, the role of blockchain in safeguarding medical records, and the development of privacy-preserving analytics. Emphasis will also be placed on strengthening cross-border data protection policies and promoting inclusive design practices to serve vulnerable patient populations more effectively. In addition to its focus on healthcare fraud, the Health 2.0 Conference will continue to offer its rich slate of content across healthcare innovation, patient-centric care, genomics, mental health, wearable tech, and precision medicine. From regulatory updates to wellness tech showcases and public health preparedness, the event provides a comprehensive snapshot of the future of healthcare. The upcoming 2026 edition of the healthcare event will also include networking pavilions, exhibit booths, and recognition session to celebrate breakthroughs in ethical and scalable healthcare delivery. Delegates from over 40 countries are expected to attend, reinforcing the conference's reputation as a truly international forum for learning and leadership. With the conference's theme centered on transformation through trust, Health 2.0 Conference seeks to catalyze long-term change by encouraging collaboration across silos. The emphasis on addressing fraud is not only timely but necessary for creating a resilient healthcare infrastructure capable of withstanding future disruptions. About Health 2.0 Conference The Health 2.0 Conference is a global convergence of healthcare professionals, policymakers, technologists, researchers, and investors dedicated to shaping the future of healthcare delivery. Through its immersive programming and action-oriented discussions, the conference promotes innovation while tackling the pressing issues of accessibility, affordability, and accountability. The 2025 edition of the health conference will feature an expanded focus on tackling fraud and scams in the health sector, making it one of the few healthcare gatherings to place this growing threat at the center of its agenda. For more details, visit Bhawna Banga Health 2.0 Conference +1 888-575-9240 email us here Visit us on social media: LinkedIn Instagram Facebook YouTube X Legal Disclaimer: EIN Presswire provides this news content 'as is' without warranty of any kind. We do not accept any responsibility or liability for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this article. If you have any complaints or copyright issues related to this article, kindly contact the author above.
Yahoo
21-06-2025
- Business
- Yahoo
Trust in AI is growing in finance, especially behind the scenes
This story was originally published on CX Dive. To receive daily news and insights, subscribe to our free daily CX Dive newsletter. A majority of customers trust the use of AI in behind-the-scenes tasks at financial institutions, according to a TD Bank survey conducted by Ipsos released Tuesday. Among the 2,500 U.S. consumers polled, 70% are comfortable with technology being used for fraud detection, and 64% are comfortable with it being used in credit score calculations. Consumers also believe that AI should offer more ease. Two-thirds believe it can expand access to financial tools, and nearly half expect benefits from AI like 24/7 banking access. As consumers have become more familiar with AI tools, their trust in the technology has slowly grown. Nearly 7 in 10 consumers say they are at least somewhat familiar with AI — a finding seen in other surveys, too. Notably, half of consumers trust that AI will provide reliable, competent information, trusting AI just as much as news stations. But consumers are more comfortable with AI in specific use cases and the more complex or sensitive the matter, the more they want to speak to a human or know that a human will be reviewing AI before making any decisions. Consumers are less inclined to want to only use AI when it comes to tasks that one might typically use a financial adviser for, according Ted Paris, EVP, TD Bank AMCB, and head of analytics, intelligence & AI. When it comes to personal finance, 3 in 5 of consumers were comfortable with the idea of using AI financial tools for budgeting and automating savings goals. But less than half were comfortable with more complex tasks like retirement planning and investing. Banks enjoy high consumer trust — more than 4 in 5 consumers trust banks for accurate information. As they deploy AI, it's important that they maintain that, Paris said. 'What's probably the key piece, is creating and enabling and allowing customers and colleagues to feel that they can trust the outcomes of what this capability then generates,' Paris said. One of the ways TD Bank is approaching this is by always having a human in the loop, meaning that the output of an AI solution will be passed through some internal expert before going to a client. 'We need to make sure that first, anything that we're doing is directed toward a particular need,' Paris said. 'We need to make sure that this is going to meet all hurdles that we would set, legal, regulatory, for security and privacy.' Sign in to access your portfolio
Yahoo
20-06-2025
- Business
- Yahoo
Curinos: Bringing AI out of the lab and into practical business environments
Just how can banks balance innovation with regulation and where might the greatest ROI come from as banks ramp up their adoption of AI? Those are just two of the hottest topics tackled by Olly Downs of Curinos when he sat down with RBI. On ROI, he says that the most exciting returns are likely to come from AI solutions that solve operational challenges and drive sustainable growth. He joined Curinos in May 2023 as Chief Data Scientist to oversee the continued development of AI and machine learning within its solutions. Almost every press release relating to a senior hire in the sector claims that the appointment in question represents a strategic hire. But in Downs case, the release at the time was justified. When it comes to the merging sciences of machine learning, Downs has been the inventor on 41 US patents spanning machine learning, personalisation, location-based services and quantum computing. Indeed, Downs published his first academic paper on generative AI as long ago as 1999 and so has had a ring side seat through multiple evolutionary stages of AI. So, AI in the banking sector is far from a novelty and has been used to enhance fraud detection, in customer support and has been deployed in credit and insurance underwriting. The use of AI for chatbots is also not new but the quality of the technology has improved massively in recent years. What is different this time is the challenge of bringing AI out of the lab and into a wider range of practical business environments while meeting the banking sector's unique regulatory challenges. Another novelty dates back to around 2022 and the hype around ChatGPT. This served to heighten interest among consumers and reignited interest in AI from financial institutions. It also attracted the attention of regulators and governments. Curinos' mission is actually quite straightforward to summarise: it leverages proprietary data, decision tolls and AI to help its clients optimise their go-to-market decisions. Rather stating the obvious, the difficult bit is executing on that mission. And it does so for a huge number of prominent clients. Its 1,500+ clients globally include 46 of the top 50 US banks, the big six Canadian banks, over 800 US community banks and credit unions and 42 of the largest 50 mortgage lenders. That sort of range of clients gives Curinos insight into over $7trn in deposit data, $3trn in mortgage originations, and $9bn in marketing spend. Moreover, that volume of data allows Curinos to provide tools and insights that are uniquely representative of real-world financial behaviour. While most financial authorities have not issued AI regulations specific to banks as existing regulations address most of the possible risks, there remains the threat of unnecessary new regs. 'AI can be delivered without invoking the disruption of AI regulation. The biggest enablers as a result of AI in banking lie around service, around customer experience and customer engagement and in back-office automation. Most of the value lies in these areas, which don't need to provoke the high or elevated risk levels of the EU AI act, for example,' says Downs. 'I think it's an exciting future. We have the regulatory guardrails in place now, in Europe, the UK and in the US. we've been dealing with models and analytics based decisioning in banking for a very long time, and some of the structures already in place put some very healthy constraints and checks and balances around the type of decisioning and analytics that can generate bias. I actually believe we're in quite good shape in the banking industry overall. 'Human relationships don't scale very well. There's a lot of cost in the bank, branch banker to customer relationship that can only serve today some of the highest value and most profitable customers. The real opportunity here in banking relationships with the customer is the ability to scale a highly engaged, mutually beneficial, high value relationship for the long term, using AI as an enabling technology.' ROI from optimising AI strategy is most likely in three areas, says Downs. In the back-office, there remains scope to take friction out of operations in areas where there is manual and error prone processes. The second is in service and customer experience and taking the friction and cost out of customer engagement at scale, particularly digital customer service, online customer service, chat and voice customer service. 'And then thirdly, where you can really impact customer value lies in personalisation or in the commercial or business setting, sort of individualisation of experience and driving cross sell, upsell and long-term customer value." "Curinos: Bringing AI out of the lab and into practical business environments" was originally created and published by Retail Banker International, a GlobalData owned brand. The information on this site has been included in good faith for general informational purposes only. It is not intended to amount to advice on which you should rely, and we give no representation, warranty or guarantee, whether express or implied as to its accuracy or completeness. You must obtain professional or specialist advice before taking, or refraining from, any action on the basis of the content on our site. Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Yahoo
19-06-2025
- Business
- Yahoo
Will the AI-Infrastructure Boom Lift C3.ai's Application Demand?
Inc. AI finds itself at a pivotal moment as the broader AI-infrastructure boom gains traction across the enterprise landscape. While hyperscalers like Microsoft, AWS and Google build the foundation, is focused on delivering turnkey AI applications that solve practical business problems, from predictive maintenance to fraud over 130 AI applications deployed and more than 600 joint account efforts underway with Microsoft alone, is counting on the infrastructure boom to fuel downstream demand for its software. In fiscal 2025, posted 25% year-over-year revenue growth, bolstered by deepening partnerships and strong adoption across sectors like manufacturing, defense and life sciences.A key enabler is its expanding partner ecosystem. In fourth-quarter fiscal 2025, 73% of bookings came through partners, with a 419% year-over-year surge in partner-supported deals. Investments in demo licenses and prioritized engineering services, together accounting for nearly overall revenues, are helping partners showcase solutions the broader infrastructure buildout continues to attract headlines and capital, thesis is that enterprise value accrues not at the hardware or model level, but where actionable AI is deployed. As enterprises seek production-ready solutions atop robust infrastructure, differentiated platform could see a long-awaited we believe unlocking this potential depends on ability to consistently execute, accelerate deal conversions and drive deeper adoption of its applications. If the company delivers on these fronts, the AI infrastructure boom will not just boost data processing, it could be the catalyst that propels into the next phase of meaningful top-line growth. While takes the position of a pioneer in turnkey enterprise AI applications, it is not alone in targeting the growing demand built atop AI infrastructure. Two noteworthy competitors are Palantir Technologies Inc. PLTR and Snowflake Inc. long known for government analytics contracts, has made a decisive shift into commercial AI with its Artificial Intelligence Platform. Like Palantir focuses on real-world use cases, particularly in manufacturing, logistics and defense. It also benefits from early mover advantage and deep client relationships in sensitive, high-barrier though rooted in cloud data warehousing, is evolving toward a full AI-data platform. Its acquisition of AI startups and rollout of Snowflake Cortex shows a pivot to infuse AI natively into enterprise workflows. While not a pure-play application provider, Snowflake's tight integration with cloud infrastructure and data pipelines positions it as a formidable challenger. AI's shares have gained 8% in the past three months compared with the industry's growth of 5%. Image Source: Zacks Investment Research Despite the recent gain, AI is priced at a discount relative to its industry. It has a forward 12-month price-to-sales ratio of 6.7, which is well below the industry average. Image Source: Zacks Investment Research The Zacks Consensus Estimate for fiscal 2026 loss per share has narrowed to 37 cents (compared with a loss of 47 cents a year ago) in the past 30 days. Moreover, the consensus mark for fiscal 2027 loss per share has narrowed to 16 cents from a loss of 45 cents in the same time frame. Image Source: Zacks Investment Research The stock currently carries a Zacks Rank #2 (Buy). You can see the complete list of today's Zacks #1 Rank (Strong Buy) stocks here. Want the latest recommendations from Zacks Investment Research? Today, you can download 7 Best Stocks for the Next 30 Days. Click to get this free report Inc. (AI) : Free Stock Analysis Report Snowflake Inc. (SNOW) : Free Stock Analysis Report Palantir Technologies Inc. (PLTR) : Free Stock Analysis Report This article originally published on Zacks Investment Research ( Zacks Investment Research Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
