AI capabilities - FinTecBuzz https://fintecbuzz.com Fintech News Wed, 04 Sep 2024 09:15:22 +0000 en-US hourly 1 https://wordpress.org/?v=6.5.5 https://fintecbuzz.com/wp-content/uploads/2019/04/cropped-Original-black-FinTech-512-32x32.png AI capabilities - FinTecBuzz https://fintecbuzz.com 32 32 The Four Stages of Card Program Maturity: Card Tech Modernization https://fintecbuzz.com/card-program-modernization-stages/ Wed, 28 Aug 2024 12:30:26 +0000 https://fintecbuzz.com/?p=64105 Explore the four stages of card tech evolution and unlock significant benefits for your institution.

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Over the past three years, the team at Zeta has engaged with hundreds of leaders from over 50 card issuers across America. During these conversations, recurring questions emerged about managing portfolios on legacy systems, migrating to modern card programs and understanding the perspectives of other card executives. These themes are addressed in a comprehensive report by Zeta and Datos Insights titled “Card Program Evolution: Escaping the Legacy Card Tech Hamster Wheel,” where they conducted an in-depth study involving a dozen executives managing significant card programs.

Key Takeaways from the Report
The report defines four stages of card tech modernization, highlighting the evolution from legacy-dependent systems to fully modern infrastructures.

In Stage 1: Legacy Dependent, innovation is slow and reliant on processors, with customer experience (CX) limited to banker interactions or basic mobile apps. Data access is poor and slow, and costs are high for both service and maintenance. Transitioning to Stage 2 is achievable through incremental technological changes, but creating a significant impact requires more extensive modernization.

Stage 2: Legacy with APIs or Sidecar Programs sees slightly faster innovation, though control over the roadmap remains limited. CX lags behind leading programs, data access is still limited and costs are reduced but may require parallel systems. Remaining in Stage 2 exposes issuers to significant compliance and regulatory risks.

In Stage 3: Modernized with Some Legacy, innovation occurs mostly in-house, except where legacy-dependent. CX can be personalized with some AI capabilities, real-time data access is available but limited in scope and costs are lower except for legacy maintenance and changes. Moving into this stage is particularly challenging due to the complexity of legacy systems.

Stage 4: Fully Modern represents a state where almost all innovation and changes are handled in-house. CX is highly personalized and predictive, leveraging maximized AI capabilities. Real-time data access is available across internal and external streams, and costs for CX and maintenance are low with simpler billing structures. Access to data is crucial for transformation, which is difficult to achieve without modern technology.

Transitioning through these stages reveals that AI has the potential to be transformative, requiring platforms that support real-time data access and rapid iteration of customer experiences. Breaking down the shift to modernization into stages is recommended for most financial institutions since extensive changes are required across various organizational areas.

Challenges and Risks of Legacy Platforms
Bankers face several challenges and limitations to their card programs when they are stuck in their legacy systems. There are six primary risks associated with maintaining legacy platforms. Consumers today need their demands met with key features that provide transparency, control, immediacy and intuitive interactions from their financial institutions. Another risk of staying in a legacy system is the decline of COBOL programmers, which requires very complex coding and poses significant risks for reacting to regulations and managing portfolios. With the rise of AI, there will be new demands for more flexible platforms. Additionally, real-time processing has already begun replacing batch processing as the default. Branch networks have become less competitive, which means digital incumbents are increasingly challenging traditional institutions. Lastly, regulatory changes add complexity and risk, diverting time from innovation.

Functionalities of Next-Gen Processing Platforms
Next-generation processing platforms offer several key functionalities that address these challenges. Cloud-native solutions provide significant advantages over cloud-based ones. An API-first approach ensures easier implementation and faster development. Real-time data access is ranked as the top benefit by bankers. Low-code/no-code design enables staff to develop solutions without technical expertise, while a microservices architecture increases flexibility, scale, and speed to market. Modern user interfaces reduce training needs with intuitive navigation, and modern programming languages address the shortage of COBOL programmers. Broad card program controls allow issuers to make in-house changes, minimizing change requests and enhancing control over the product roadmap.

Initiate the Modernization Journey
Modernizing card programs is a complex but necessary journey for financial institutions to remain competitive and meet evolving consumer demands.
  These four stages of modernization serve as a roadmap for issuers to navigate this transformation, leveraging modern technology to enhance customer experiences, improve efficiency and stay ahead in a rapidly changing landscape.

Staying at the initial stages poses significant risks, including the inability to handle diverse payment types and compliance challenges. The frustration of migrating between legacy platforms and the limitations they impose underscore the need for comprehensive modernization. But as financial institutions move through this journey, each stage unlocks significant benefits.

Fintechs are using modern issuance platforms to innovate by reimagining cards as products, business models and data engagement layers. Additionally, banks in the corporate card space are embracing modern technology to stay competitive, allowing fintechs to develop capabilities around their cards.
This decentralized innovation enables rapid iteration and interaction with other systems, providing significant benefits.

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Gary Singh, President, North America at Zeta.

Gary Singh is the President, North America at Zeta. A 20+ year silicon valley industry veteran, Gary has an extensive knowledge about the fintech industry and holds multiple patents in the mobile and wireless industry. At the core, Singh is a business and product guy, who understands how to build and take new and innovative products and services to disrupt status quo markets. Prior to joining Zeta, Singh was the Chief Revenue Officer at Ondot Systems. He has also held executive level positions at Obopay, Nokia Financials Services and Aruba Networks. He comes with over a decade of experience at Zebra (through multiple acquisitions — Motorola Solutions enterprise division and Symbol technologies), where he helped pioneer the WiFi market to automate supply chain operations. At Zeta, Singh is responsible for the company’s go-to-market, operations, growth and overall financial performance in North America.

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FinTech Interview with Srikrishnan Ganesan, Co- Founder and CEO of Rocketlane https://fintecbuzz.com/fintech-interview-with-srikrishnan-ganesan/ Tue, 20 Aug 2024 13:30:33 +0000 https://fintecbuzz.com/?p=63713

Learn from Srikrishnan Ganesan, Co-Founder and CEO of Rocketlane, as he shares insights on AI’s impact on professional services in this FinTech interview.

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Srikrishnan Ganesan, Co- Founder and CEO of Rocketlane

Srikrishnan Ganesan is the co-founder and CEO of Rocketlane, a purpose-built PSA and client onboarding platform that helps businesses deliver predictable outcomes, accelerate time-to-value, and improve team utilization and project profitability. Sri has a strong passion for customer experience (CX) and startups. His professional journey includes founding and scaling SaaS businesses over the last decade. Prior to Rocketlane, he co-founded Konotor, a mobile-first user engagement platform that was acquired by Freshworks in 2015. This acquisition turned out to be a significant growth vector for Freshworks, with the product evolving into what is now known as Freshchat.

Srikrishnan, it’s great to have you with us. Can you start by sharing a bit about your professional journey and what led you to co-found Rocketlane?

I did my bachelor’s in Computer Science & Engineering and went to one of India’s top B-schools – IIM Bangalore from 2005 to 2007. Post that I’ve been in in product companies – first building product, and later building the business. I’m a techie at heart and started my career in B2C products – across Verizon, Rediff.com, and a start-up called Jigsee. It was 2012 when I started my first start-up with a couple of close friends, and my B2B SaaS journey started with that venture. We were lucky to be acquired by Freshworks in 2015, and learned a lot about building and running a SaaS business in the 4 and a half years we spent there post-acquisition. Inspired by the success and impact I saw at Freshworks, I started Rocketlane in 2020 with the same co-founders – Vignesh and Deepak.

How does Rocketlane plan to utilize the Series B funding to advance its AI integration in the professional services sector?

We’re integrating advanced AI capabilities to revolutionize project delivery, governance, and operations with copilot experiences, automations, forecasting, insights, and recommendations.

What specific inefficiencies in customer onboarding and project management does Rocketlane aim to address with its AI-driven approach?

In our previous roles at Freshworks, we noticed that the customer onboarding phase was under-serviced, crucial for gaining customer confidence but plagued by gaps in collaboration and visibility, making value demonstration challenging. Existing tools failed to provide cohesive visibility or enforce our playbook, leading to siloed work streams and inevitable escalations. Realizing the opportunity, we spoke to other companies facing similar issues and decided to create Rocketlane—a product designed specifically for customer onboarding and client project delivery, ensuring the right visibility and experience throughout the client delivery journey.

What strategic advantages does Rocketlane foresee by integrating AI into operations, project delivery, governance, and insights?

Rocketlane is able to make better governance, increased automation and efficiency available out-of-the-box for its customers through AI vs. having to set up elaborate rules and integrations to accomplish the same. This means more customers will use the intelligent capabilities and leverage the full power of a PSA solution. This ultimately means Rocketlane customers will see more success with the platform and be happy advocates.

In what ways does Rocketlane differentiate itself from traditional PSA software in terms of enhancing client satisfaction and project success rates?

Rocketlane is purpose-built for professional services delivery. It combines project management, resource management, communication and collaboration, reporting and analytics, financial management, and project automation capabilities into one sleek platform. Our customers do not need to switch tabs and flit between spreadsheets to keep track of projects.

How do real-time insights play a role in Rocketlane’s strategy for enhancing team collaboration and decision-making?

Real-time insights on our customers’ usage of Rocketlane help us understand how our product UX and flows impact adoption of various impactful capabilities we build for customers. The data and insights help us come up with new hypotheses, as well as validate existing ones, especially when we are able to slice and dice the data by cohort, industry, use case, etc.

How does Rocketlane ensure data privacy, accuracy, and consistency through AI automation, and what impact does this have on service quality?

Rocketlane takes the security and privacy of its customers seriously. Our services are tested automatically on every SDLC lifecycle. Thousands of tests ensure that the quality of the software we release to our customers meets our stringent guidelines. All customer data is physically or logically separated from each other and encrypted at rest and in transit. These and other controls we’ve implemented ensure we can meet compliance standards like SOC 2, ISO 27001, GDPR, and HIPAA while maintaining a service SLA of 99.9% or higher.

How does Rocketlane balance automation with maintaining a personalized client experience in professional services?

Rocketlane’s automation and intelligent capabilities focus on providing the right alerts and insights in a timely manner, nudging users on actions, or providing an automated starting point for a personalized engagement. Customers use these automations, templates, and more as a nudge or an initial draft that they can work on top of to then personalize the experience – which also could be aided by AI.

Srikrishnan, what personal strategies do you employ to stay ahead in the rapidly evolving tech landscape?

  • Spend deliberate time brainstorming with customers, partners, and internal team members to see what interesting and high impact ideas they have, and where they are seeing success with new products and technologies.
  • Thought exercises around what kind of new product or technology can disrupt Rocketlane today.
  • Part of the product/tech team is always experimenting with new technology and showcasing the experiments in weekly demos for the rest of the company to riff off on.
  • Innovation seldom occurs in isolation. Watching out for innovation in other industries can carry very important clues for what can work in your own space. So, following the media to track innovation in adjacent spaces or parallels from other industries.

Finally, do you have any parting thoughts or advice for our readers about the future of AI in professional services and project management?

AI is going to aid services and project delivery professionals in a big way – increase our efficiency, identify risks, and help us codify our ways of working better.Embrace it and enable it by ensuring the data going into these AI enabled PSA and PM systems is maintained accurately at all times. By letting AI do its part – summarization, alerts, insights, document or email generation, etc., we can focus on the “human” aspects of client project delivery, and on developing the right plays to react to the insights, warnings, and inputs from AI.

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