Hello, FinTech community. Welcome to our FinTech Top Voice Interview Series.
The latest FinTech Interview with Carolyn Duby, Field CTO, Cloudera is an interactive Q&A-styled conversation.
Carolyn is Field CTO and Cyber Security GTM Lead at Cloudera. World wide cyber security strategist helping global companies in technology, finance, and telecom leverage analytics and machine learning to reduce mean time to detect cyber security incidents, reduce cyber risk, and streamline response and investigation processes.
About Cloudera – Cloudera delivers an enterprise data cloud for any data, anywhere, from the Edge to AI. Powered by the relentless innovation of the open source community, Cloudera advances digital transformation for the world’s largest enterprises. Learn more at Cloudera.com.
Let’s start…
FinTech Insights (FTI): Hi Carolyn, welcome to the FinTech Top Voice Interview Series. Please share your tech journey with us.
Carolyn: My professional journey has spanned across various industries, touching on high-performance medical devices to cybersecurity and distributed big data solutions. Starting my career at Cadre Technologies, I put my software engineering skills to practice and was given a great opportunity to establish industry connections. My time at Cadre provided me the skills and relationships to go forth and found my own company, Pathfinder Solutions, where I was able to wear various hats. There, I was a developer, marketer, sales, and worked in training and consulting, which I found to be one of the great things about working for a small company – I was able to learn a wide variety of skills across the departments and hone my overall leadership abilities.
Next, at Dell SecureWorks, I leaned more into my passion areas, specializing in real-time cybersecurity ingestion pipelines, deepening my understanding of reliability engineering and team leadership.
This leads me to where I’m at today with Cloudera. I joined Cloudera in 2021 as a Solutions Engineer, where I leveraged big data for business transformation. I advanced to the role of Field CTO where I now advise our clients on how to best leverage their data with AI-driven transformations.
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(FTI): How do you see AI impacting how Finance Technology Solutions will evolve in the future?
Carolyn: Traditional machine learning models improve risk management, credit scoring, anti-money laundering initiatives, fraud detection, and process automation. However, the introduction of generative AI creates even more possibilities for financial service firms, allowing for personalized customer interactions via virtual assistants, automated content generation, enhanced risk and compliance assessments, and data-driven trading strategies.
Two significant AI advancements across the financial sector are in creating personalized customer experiences and in automated content creation. Across customer service, AI-powered virtual assistants and chatbots can provide tailored financial advice, product recommendations, and support, ensuring better customer experience. An example of this includes an “internal ChatGPT” trained on the customer’s procedures and documents which helps customer service reps resolve issues more quickly. There is also an increase in code assistants that adhere to the company’s coding standards and security measures, lowering risk of leaking sensitive information to a service and helping ground results in the company’s best practices.
Utilizing AI in customer experience has proven highly successful, with 80 percent of executives reporting measurable improvements in customer satisfaction, service delivery, and contact center performance after implementing AI.
As noted, another area where AI will make a substantial impact is in automated content creation. GenAI models can automatically generate a wide range of materials, including marketing content, research reports, investment summaries, and more. By analyzing data, news, and market trends, these models produce high-quality content quickly and efficiently, freeing up human resources for more strategic tasks.
(FTI): With the rapid increase of AI in financial services, what AI use cases are you seeing adopted by customers?
Carolyn: Financial service customers leverage GenAI in various ways, many of which to enhance customer experience, manage risk, prevent financial crime, boost operational efficiency, and streamline regulatory compliance. Common use cases are automating compliance systems to help financial service firms meet regulatory requirements, as well as preventing financial crime through additional protection and monitoring of security threats.
A notable success story of Cloudera’s is centered around a financial institution in Southeast Asia. This organization has utilized advanced data platforms to consolidate data sources and integrate AI/ML capabilities into their workflows, which led to the development of a proprietary application that sends over 100 personalized notifications through their mobile banking app. These notifications inform customers about opportunities such as new credit card offers or loan eligibility and have achieved click-through rates of up to 50%.
By enhancing their data platforms by employing AI/ML for data-driven decision-making, Cloudera’s solutions enabled the organization to nearly double campaign conversion rates and increase its annual revenue by over SGD 100 million.
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(FTI): As the financial services industry is facing an increasingly complex regulatory landscape, particularly when it comes to data privacy and the use of artificial intelligence, how can a hybrid cloud architecture and AI help financial service companies manage these requirements?
Carolyn: The financial sector is encountering numerous challenges regarding regulation and data privacy, particularly with the emergence of laws like the EU AI Act and the Digital India Act. These guidelines aim to promote the ethical and responsible use of AI and require a strong infrastructure to effectively comply. In addition to general AI compliance, financial institutions also face specific data privacy challenges, influenced by regulations such as GDPR, CCPA, LGPD, APPI, and PIPL, which dictate how personal data should be handled.
In today’s complex landscape, a hybrid cloud architecture is necessary for juggling general AI compliance as well as privacy and other risk regulations, which are especially prominent in the financial sector. A hybrid cloud architecture integrates the advantages of on-premises and cloud environments, providing a flexible, secure, and scalable data management solution. This enables financial institutions to ensure compliance, bolster security, and adapt to regulatory changes while also optimizing costs and ensuring uninterrupted operations.
Some advantages of a hybrid cloud infrastructure include:
- Data Sovereignty and Localization: Privacy laws often require certain data to be stored within specific regions. A hybrid cloud setup allows institutions to keep sensitive data on-premises or in private clouds within those jurisdictions, while still utilizing public cloud resources for less sensitive tasks.
- Granular Data Control: Hybrid cloud solutions enable financial institutions to implement detailed access controls and data classification systems, facilitating better management of personal data and compliance with data subject rights like those mandated by GDPR and CCPA.
- Enhanced Security Measures: The hybrid cloud model supports robust security strategies, such as encryption, tokenization, and data masking, which are essential for safeguarding personal data and adhering to privacy regulations.
- Compliance Monitoring and Reporting: Many hybrid cloud platforms come with tools that enable ongoing compliance monitoring and automated reporting, helping financial institutions maintain transparency and meet regulatory obligations.
- Disaster Recovery and Business Continuity: The capacity of a hybrid cloud to distribute workloads across various environments offers a solid foundation for disaster recovery and business continuity, ensuring that personal data remains secure and accessible during system failures or cyberattacks.
(FTI): What are your predictions for the Fintech domain for 2025, as related to AI?
Carolyn: It’s expected that financial service firms will begin to take a more pragmatic approach to AI as data, and data quality, continue to become more important. Businesses will cease buying into the hype of Gen AI and instead focus on strategizing their investments to align with business goals in order to receive greater ROI. Forrester found that many organizations have struggled to find the ROI after launching AI projects, with nearly half of AI decision-makers saying their organizations expect ROI on AI investments within one to three years, while another 44% expect a longer timeframe.
With organizations facing the challenge of needing to show measurable outcomes of AI projects to justify the cost, they will need to begin to adopt the technology more strategically.
With financial service institutions being early adopters of GenAI, we’re seeing a prominent shift rippling through the industry as more firms are recognizing that the value of GenAI lies in gaining knowledge and insights at scale. It’s become clear that without good data, AI models are unable to run successfully. Thus, businesses that are most likely to slow down and ensure the data being utilized is high quality and meets all the legal requirements will see more prominent growth.
(FTI): How have you seen AI benefit financial services when it comes to data security?
Carolyn: Data privacy and security in the financial sector demand rigorous protection measures for sensitive information. AI allows for the rapid analysis of complex data to identify potential risks or regulatory and compliance issues. This capability allows financial institutions to generate detailed assessment reports swiftly, ensuring they remain compliant with evolving regulations and mitigate risks effectively.
Some additional areas where AI is also especially helpful includes anomaly detection, which is very important to data security, and in discovering sensitive data patterns. In the future I expect to see an increase in AI-driven agents assisting investigators and data governors to ensure data security across all industries, but especially within the financial services.
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Thank you Carolyn for sharing your Fintech comments. We look forward to having you again at Fintech top voice series.
