Zi Cloud’s Post

The Global AI In Banking Market is projected to grow from USD 20.87 Billion in 2023 to USD 310.79 Billion by 2033, with a CAGR of 31.01%. AI is enhancing customer satisfaction, security, and productivity in banking. Operating costs are reduced through automation, and AI-driven chatbots provide 24/7 customer support. Credit scoring algorithms are more precise with AI. North America leads the market, while Asia Pacific is growing rapidly. Major vendors include IBM, Microsoft, and Google Cloud.

The BaaS Revolution: Disruption and Opportunities in Financial Services - by Twimbit 📈 BaaS growth opportunities -BaaS is expected to grow customer base for banks by 50% by 2024 -BaaS represents a USD 7 trillion opportunity by 2030 -As of 2022, more than 30% of customer transactions occur outside the bank’s platform -Banks could cut costs by up to 30% with BaaS adoption. -BaaS is projected to increase banks’ CASA by approximately 5% to 10% by 2024 -Banks leveraging BaaS can attain a CAC as low as USD 5 to 35 per customer, versus the traditional of USD 100 to USD 200 per customer ☑️ 6 steps to successfully implement BaaS 1. Customer-centric approach Keep the end customers in mind while designing the BaaS offerings. Focus on delivering seamless and personalized experiences to enhance customer satisfaction for both partners and end-users. 2. Ensure regulatory compliance Ensure compliance with all relevant financial regulations and data privacy laws. Collaborate closely with legal and compliance teams to navigate the regulatory landscape and address any potential challenges. 3. API design and development Develop robust and well-documented APIs that are easy to integrate and use. Prioritize security to protect sensitive customer data and transactions, while adhering to industry-standard encryption and authentication protocols. 4. Infrastructure and scalability Invest in a scalable and reliable infrastructure that can handle increased API traffic and ensure high availability. Consider cloud-based solutions that offer flexibility and scalability. 5. Partner onboarding and support Establish a streamlined onboarding process for potential partners, making it easy for them to access and integrate the APIs. Offer comprehensive developer documentation, training, and support to assist partners during the integration process. 6. Continuous API performance monitoring Track API usage, partner engagement, and customer feedback with comprehensive analytics. Monitor API performance for continuous improvement. Foster an innovative and flexible culture and adapt BaaS offerings based on feedback and market trends. 👉 https://lnkd.in/e42gF-fw #Fintech #Bank #BaaS

The BaaS Revolution: Disruption and Opportunities in Financial Services - by Twimbit 📈 BaaS growth opportunities -BaaS is expected to grow customer base for banks by 50% by 2024 -BaaS represents a USD 7 trillion opportunity by 2030 -As of 2022, more than 30% of customer transactions occur outside the bank’s platform -Banks could cut costs by up to 30% with BaaS adoption. -BaaS is projected to increase banks’ CASA by approximately 5% to 10% by 2024 -Banks leveraging BaaS can attain a CAC as low as USD 5 to 35 per customer, versus the traditional of USD 100 to USD 200 per customer ☑️ 6 steps to successfully implement BaaS 1. Customer-centric approach Keep the end customers in mind while designing the BaaS offerings. Focus on delivering seamless and personalized experiences to enhance customer satisfaction for both partners and end-users. 2. Ensure regulatory compliance Ensure compliance with all relevant financial regulations and data privacy laws. Collaborate closely with legal and compliance teams to navigate the regulatory landscape and address any potential challenges. 3. API design and development Develop robust and well-documented APIs that are easy to integrate and use. Prioritize security to protect sensitive customer data and transactions, while adhering to industry-standard encryption and authentication protocols. 4. Infrastructure and scalability Invest in a scalable and reliable infrastructure that can handle increased API traffic and ensure high availability. Consider cloud-based solutions that offer flexibility and scalability. 5. Partner onboarding and support Establish a streamlined onboarding process for potential partners, making it easy for them to access and integrate the APIs. Offer comprehensive developer documentation, training, and support to assist partners during the integration process. 6. Continuous API performance monitoring Track API usage, partner engagement, and customer feedback with comprehensive analytics. Monitor API performance for continuous improvement. Foster an innovative and flexible culture and adapt BaaS offerings based on feedback and market trends. 👉 https://lnkd.in/e42gF-fw #Fintech #Bank #BaaS

BMO, North America’s eighth largest bank by assets, has partnered with Dynatrace, a leader in unified observability and security. The partnership between BMO and Dynatrace is rooted in the mutual goal of scaling digital capabilities to provide superior customer experiences worldwide. Dynatrace offers a unified observability and security platform that incorporates technologies like the Grail™ data lakehouse and Davis® hypermodal AI. This platform is designed to provide businesses with deep insights into their digital environments, facilitating continuous improvement and innovation. Dynatrace’s tools are instrumental in enabling companies like BMO to refine their digital services and deliver exceptional user experiences. The integration of Dynatrace’s Grail and Davis AI into BMO’s operations has led to a significant reduction in the bank’s mean time to identify (MTTI) issues and root cause analysis (RCA) timelines by 80%. This efficiency has freed up valuable human resources, allowing BMO to concentrate on providing strategic innovation and expert advice to its customers. BMO Chief Technology, Resiliency, Experience and Operations Officer Angela Sim said, “To drive our digital-first strategy and continue delivering speed and scale for our customers, BMO innovates rapidly …

Embracing New Data Management Architectures: Integrating AI Across Banking Processes I meet a number of senior bankers in the course of my daily interactions. A constant theme in the conversations is around using AI, especially Gen AI. In the past, it was more around specific use cases of where AI will be relevant. As a bank CIO recently said to me, 'I envision a near future where every banking business process will be integrated with AI', the focus today is on making AI integration a default in every banking process." In the fast-paced banking world, adopting new data management architectures that integrate AI into key business processes is transformative. This shift revolutionizes operations, decision-making, and customer service. Implementing it requires a rethink on the data management architectures. Imperatives of New Data Management Architectures 1. Unified Data Platforms: Consolidate data from various sources, enabling comprehensive AI analysis for deeper insights and accurate predictions. 2. Real-Time Data Processing: Facilitate instant, data-driven decisions in fraud detection, loan approval, personalized marketing and customer service. 3. Scalability and Flexibility: Cloud-based architectures offer scalability, adapting to growing data volumes and evolving needs. 4. Enhanced Data Governance and Security: Ensure robust data governance, security, and regulatory compliance. 5. Seamless Integration: Integrate with various AI tools and platforms for optimal AI technology utilization. Importance of an Enterprise Data Model (EDM) in creating the Unified Data Platform: 1. Standardization: Ensures data consistency and accuracy across systems. 2. Data Quality: Maintains high data quality for effective AI applications. 3. Interoperability: Supports integration of diverse data sources for comprehensive analysis. 4. Efficiency: Streamlines data retrieval and processing, enhancing AI performance. One example of AI benefit and role of EDM: A bank using real-time data processing and AI can swiftly respond to customer queries, offer personalized loans, and ensure regulatory compliance. An EDM supports this by ensuring data consistency and quality. Adopting these architectures, supported by a robust EDM, is crucial for transforming banking operations. Banks that embrace this change will enhance efficiency, customer satisfaction, and industry leadership. #Banking #AI #DataManagement #EnterpriseDataModel #DigitalTransformation #CustomerExperience #Digitalbanking #Digital #Financialservices #OperationalEfficiency #RiskManagement #Innovation #Fintech #Apar #BigTapp

Daily Empathy Post #2584 (September 7) "In fact, new tech – cloud platforms and new data tools like Artificial Intelligence and Machine Learning are empowering CTOs and CIOs to balance the need for standardisation as well as enabling customer specific personsalisation, delivering in tandem, both the revenue and the cost agenda. The problem is that empathy is often associated with face-to-face human contact. Banks need to inject creative thinking. And that’s where creating a culture of innovation becomes key. Ideas can come from anyone within the bank and shouldn’t be limited to just innovation teams." https://lnkd.in/d6NpTmdK

How can my institution take the lead on AI in banking? Four steps banks can take to build a roadmap to AI adoption success According to our World Retail Banking Report, 96% of banks score medium to low on the AI readiness scale, assessed from both a technology and business perspective. Notably, only 6% of these firms have an appropriate plan for establishing an AI adoption roadmap. Still, significant impediments to intelligent transformations remain. These include legacy systems, fragmented data, regulatory challenges, skill shortages, and return on investment concerns. In this article we explore how banks overcome such barriers by developing an appropriate plan to guide AI adoption initiatives in order to positively benefit the bottom line. #AIRoadmap #IntelligentTransformation #Data #LanguageModel #AutonomousBanking #Cloud #Agility

Google Cloud's new Banking Survey shed light on how GenAI can drive revenue growth through improved investment research, more effective marketing, and better customer acquisition and retention strategies. It's crucial for banking leaders to consider how GenAI can be integrated strategically and responsibly into their operations. From updating AI Data Policies to addressing data security concerns and preparing for regulatory changes, there's a lot to unpack. Let's chat about the potential opportunities this technology holds for your organization!

Embracing New Data Management Architectures: Integrating AI Across some 60+ Banking Processes Have met more than 50 CIO's / Chief Data officer's across 15 countries in past 4 months and a constant message is around using AI, especially Gen AI across the now identified 60+ Banking processes bringing Automation, Efficiency, Analytics and STP. In past, it was more around specific use cases of where AI will be relevant. Today CIO's are saying - 'I envision a future where every banking business process will be integrated with AI', the focus today is on making AI integration a default in every banking process." In the fast-paced banking world, adopting new data management architectures that integrate AI into key business processes is transformative. This shift revolutionises operations, decision-making, and customer service. Implementing it requires a rethink on the data management architectures. Imperatives of New Data Management Architectures 1. Unified Data Platforms: Consolidate data from various sources, enabling comprehensive AI analysis for deeper insights and accurate predictions. 2. Real-Time Data Processing: Facilitate instant, data-driven decisions in fraud detection, loan approval, personalized marketing and customer service. 3. Scalability and Flexibility: Cloud-based architectures offer scalability, adapting to growing data volumes and evolving needs. 4. Enhanced Data Governance and Security: Ensure robust data governance, security, and regulatory compliance. 5. Seamless Integration: Integrate with various AI tools and platforms for optimal AI technology utilization. Importance of an Enterprise Data Model (EDM) in creating the Unified Data Platform: 1. Standardization: Ensures data consistency and accuracy across systems. 2. Data Quality: Maintains high data quality for effective AI applications. 3. Interoperability: Supports integration of diverse data sources for comprehensive analysis. 4. Efficiency: Streamlines data retrieval and processing, enhancing AI performance. One example of AI benefit and role of EDM: A bank using real-time data processing and AI can swiftly respond to customer queries, offer personalized loans, and ensure regulatory compliance. An EDM supports this by ensuring data consistency and quality. Adopting these architectures, supported by a robust EDM, is crucial for transforming banking operations. Banks that embrace this change will enhance efficiency, customer satisfaction, and industry leadership. #Banking #AI #DataManagement #EnterpriseDataModel #Fintech #DigitalTransformation #Innovation #CustomerExperience #Digitalbanking #Digital #Financialservices #Payments #OperationalEfficiency #RiskManagement Apar Technologies BigTapp Analytics Venkat Narayanan (LVN) Sai Sudhakar Rohit Gandhi

🌟 Transforming the financial services landscape across the Americas! 🌎 Discover how TD Bank Commercial Banking and other leading companies are leveraging AI and cloud technologies to drive innovation and efficiency. Learn about their journey, and the groundbreaking solutions they implemented. Whether it's enhancing customer experiences or streamlining operations, these success stories offer valuable insights for anyone in the financial sector. Read now and stay ahead in the digital transformation game! http://msft.it/6044lLGY6 #FinTech #AI #IndustryTransformation