AI's Impact on Banking and Financial Innovation

Pleased to share the result of a groundbreaking study I participated recently - "AI Takes Center Stage: Survey Reveals Financial Industry’s Top Trends for 2024" by Kevin Levitt at NVIDIA.   Here are some key insights:   ✅ 91% of financial services companies are either assessing or already using AI to drive innovation, improve efficiency, and enhance customer experiences. ✅ Top AI Use Cases in Financial Services: Portfolio optimization Fraud detection Risk management Generative AI gaining popularity for uncovering new efficiencies. ✅ 55% actively seeking generative AI workflows, with applications ranging from marketing to synthetic data generation. ✅ AI impact across departments: Operations Risk and compliance Marketing ✅ AI is delivering results with 43% reporting improved operational efficiency and 42% gaining a competitive advantage. ✅ Data-related challenges now take the spotlight, including privacy, sovereignty, and scattered global data. ✅ Despite challenges, 97% of companies plan to invest more in AI technologies. Focus areas include identifying additional use cases, optimizing workflows, and increasing infrastructure spending. ✅ 86% report a positive impact on revenue, 82% note reduced costs, and 51% strongly agree that AI is crucial for future success. ✅ To build impactful AI, financial institutions are prioritizing comprehensive AI platforms, collaborative environments, and high-yield use cases.   Download the Full Report: "State of AI in Financial Services: 2024 Trends" for deeper insights and results. Let's embrace the future of finance with AI!   #AITrends #GenerativeAI #FinancialServices #FinTech #CEOs #boardofdirectors   Link to the full report: https://lnkd.in/g3K5yUNV   Subscribe to #CXOSpice newsletter (https://lnkd.in/gy2RJ9xg) and #CXOSpice Youtube channel (https://lnkd.in/gnMc-Vpj) and tune in for the upcoming blog on “Pioneering Women Leadership in Tech – A Journey Through Innovation”. We will be featuring Splunk on "Resilient Customer Experience" in the upcoming episode.  

Artificial intelligence (AI) has transformed banking in both positive and negative ways, enabling more personalized customer service and a more efficient workplace, but also opening new avenues for cyberattacks and fraud. 🛡️ As a result, the banking sector faces a growing challenge of combating complex and advanced threats and increasingly sophisticated scams. 😨 McAfee's 2024 predictions underscore the looming threat, with AI taking center stage as cybercriminals harness the technology’s capabilities for dangerous deepfakes and identity theft. 😱 These concerns reflect a surge in misuse of AI, with the Sumsub Identity Fraud Report highlighting a 10x increase in deepfakes detected globally from 2022 to 2023. 📈 AI solutions can help combat fraud when customers are directly targeted in scams by enhancing accuracy, reducing investigations and mitigating compliance risk. 🙌   Many banks leverage AI for fraud prevention, expediting case resolutions and enabling focused attention on complex issues. 💯 For instance, a regional US bank employs custom and third-party machine learning solutions to combat fraud. Additionally, a leading payments card network introduced a new Generative AI model, enhancing banks' fraud detection rates by up to 300%. 🚀   Yet, the best defense lies in collaboration. 🤝 Industry-wide cooperation can enable the discovery of previously hidden data relationships to combat emerging fraud methods effectively. Additionally, sharing fraud indicators across FS firms, without revealing identifying customer attributes, can pave the way for more robust fraud prevention networks. 🔗   In this critical juncture, advanced AI techniques, collaborative efforts and fraud data sharing networks are imperative to combat the looming threat of AI-based fraud effectively. It's a necessity to safeguard financial systems, preserve trust in the digital age and create #longtermvalue. 💎   #AI #FraudPrevention #Cybersecurity #Deepfakes #IdentityTheft #GenAI #DataSharing #Collaboration   https://lnkd.in/gdwffnrp

I'm seeing a lot of financial institutions exploring how AI can reimagine the dispute workflow—but success depends on finding the right mix of automation and human insight. 🚀 Key statistics that are driving this conversation: 💸 61% of customers judge their bank more on how disputes are handled than on the fraud event itself 🏦 66% would consider leaving their bank over a poor dispute experience 🚧 14% of banking leaders cite regulatory change as the biggest issue impacting the industry Here's what I'm sharing with leaders as they map out AI in their dispute operations: - Start targeted: Use AI for complex pattern recognition and analysis, while automating routine documentation and intake tasks. - Consider pre-trained models: These can spot fraud patterns from day one, so your team gets value immediately instead of waiting months to see improvement. - Take small steps: Start with use cases like auto-routing disputes or summarizing documentation. Build team confidence before expanding into more advanced automation. - Prioritize built-in compliance: Look for solutions with transparent decisioning and strong audit trails. The underlying architecture is just as important as the outcomes. The most successful implementations I've seen combine AI technology with human expertise—where AI does the heavy lifting on analysis and pattern recognition, while your team brings judgment, empathy, and nuance to customer interactions. If your team is working through friction points in dispute resolution, message me or comment below—let's connect on practical ways AI could help solve for compliance, efficiency, and trust. ❄️ #paymentselsa #artificialintelligence #bankinginnovation #disputeresolution

I've been spending a lot of time digging into generative AI use cases in banking. The report by McKinsey & Company last year is worth a re-read. And these few points coincide with what I hear from recent conversations as well. [1] Generative AI is likely to have the biggest impact on knowledge work, particularly activities involving decision making and collaboration, which previously had the lowest potential for automation. [2] Example use cases include: - A virtual expert to augment employee performance. - Code acceleration to reduce tech debt and deliver software faster. - Production of tailored content at scale. [3] When considering the use cases to tackle, banks need to be mindful of: - The level of regulation for different processes. - Type of end user. - Intended level of work automation. - Data constraints. While AI will change how we work, we'd still need (some) humans in the near future. How Citi used generative AI to comb through 1089 pages of new capital rules on the U.S. banking sector is a great example. But I am also mindful of the challenges that remain, however, especially around reskilling/upskilling. When entry-level jobs are being eliminated, how will the younger generation and those who are new to the industry learn the art of the trade? And how can existing employees be re-trained and re-deployed in areas where their lived experiences can be leveraged? Keen to hear your thoughts. #AI #GenerativeAI #Fintech #FinancialServices #FutureOfWork | DANIELLE GUZMAN | Diana Wu David | Sabine VanderLinden | Efi Pylarinou | Dr. Martha Boeckenfeld | Penny Crosman

How will AI be used in finance? "In the short to medium term, we expect the biggest impact at incumbent financial institutions to be on internal facing tasks and improvements in productivity rather than lots of new products. Incumbents will focus on improvements in areas such as software & coding, transaction monitoring & compliance, and more. A lot of bank functions such as credit underwriting, algorithmic trading, portfolio construction, and transaction monitoring already utilize AI/deep learning applications. GenAI will create new opportunities beyond productivity improvements but some of the more blue-skies work – newer products & services, bots using tokenized money, and decentralized AI – will likely take time to build and be rolled out to market. Autonomous AI agents could turbo charge existing business models and commercial relationships and lead to the creation of new ones." Source: Citi GPS Report on Bot, Bank & Beyond: https://lnkd.in/eQgch-Zf

Clients are constantly asking me: "What's the deal with AI in banking? Why mess with a good thing?" Well, here's the thing I've learned – banking is good, but it can be better. And AI is like that secret that unlocks a whole new level of greatness. AI can personalize banking experiences, like remembering your spending habits and suggesting relevant financial products or budgeting tools. AI-powered chatbots can handle basic inquiries and resolve simple issues 24/7, freeing up human representatives for more complex matters. You get faster service, and the bank saves resources – win-win! AI can analyze your transactions in real time, spotting suspicious activity way faster than any human ever could. AI can analyze market trends and suggest investment opportunities that align with your risk tolerance and financial goals. Basically, you will have your own financial advisor 24/7. Not just that, but if you are planning a loan, AI can help you with approvals or risk management. Look, I get it. Change can be scary. But here's the reality – AI isn't here to replace the human touch in banking. It's here to upgrade it. It's about giving our amazing team the tools to be even more efficient and our customers the chance to experience banking that's faster, smarter, and more secure. So, the next time someone asks why mess with a good thing? You tell them we're not messing. We're taking banking to the next level. #ai #banking #fintech

Sam Altman Warns: AI Fraud Crisis Looms Over Financial Industry ⸻ Introduction: Altman Urges Banking Sector to Prepare for AI-Driven Threats Speaking at a Federal Reserve conference in Washington, D.C., OpenAI CEO Sam Altman issued a stark warning to financial executives and regulators: artificial intelligence is enabling a coming wave of sophisticated fraud, and many banks remain dangerously unprepared. His remarks underscore the urgency of rethinking authentication and cybersecurity protocols in an age when AI can convincingly mimic human behavior — even voices. ⸻ Key Highlights from Altman’s Remarks • Voice Authentication No Longer Secure • Altman expressed concern that some banks still rely on voice prints to authorize major transactions. • “That is a crazy thing to still be doing,” he said, emphasizing that AI can now easily replicate voices, rendering such security methods obsolete. • AI has “fully defeated” most forms of biometric or behavioral authentication — except strong passwords, he noted. • Rise in AI-Enabled Scams • Financial institutions are increasingly targeted by deepfake and impersonation-based fraud, made possible by publicly accessible AI tools. • The sophistication of these attacks is growing faster than many firms’ ability to defend against them, Altman warned. • Urgency for Regulatory Response • The comments were made in an onstage interview with Michelle Bowman, the Fed’s new vice chair for supervision. • Altman’s presence at the Fed’s event highlights how AI security is becoming a top-tier concern for financial oversight bodies. • Broader Implications for the Industry • The conversation sparked concern among attendees about the need for: • Stronger multi-factor authentication • Better fraud detection systems • Industry-wide cooperation to stay ahead of AI threats ⸻ Why It Matters: Financial Systems Face a Tipping Point Altman’s warning comes at a pivotal moment, as AI capabilities rapidly evolve while outdated financial protocols remain in place. The growing risk of synthetic identity fraud, voice spoofing, and real-time impersonation could cost banks billions — and erode customer trust. As banks digitize services, the balance between convenience and security is more fragile than ever. Altman’s call to action is clear: the financial sector must abandon obsolete verification methods and invest in advanced, AI-resilient systems — before fraudsters exploit the gap. ⸻ https://lnkd.in/gEmHdXZy