Moderated by Niharika Sharma, Head of Marketing at Hyperbots.
Niharika: Hi Mike, it’s a pleasure to have you here today to discuss the role of AI in banking and finance. Jumping right into the question, how do you perceive the current landscape of AI and human interactions within the banking and finance industry?
Mike Vaishnav: AI implementation in banking and finance has been significant and is evolving very rapidly. It has played a crucial role in enabling the banking sector to keep pace with market changes. Financial services and banking handle vast amounts of data, and AI simplifies and improves the accuracy of processing this data. For example, in commercial banking, AI helps with credit card processing and credit analysis. It enables quick and accurate decisions regarding customer creditworthiness, enhancing credit lines and financial solutions. AI-driven chatbots and virtual assistants are becoming popular for customer service, providing 24/7 support and simplifying transactions. Natural Language Processing (NLP) can analyze and understand customer needs and sentiments, making decisions based on trends in customer behavior. This helps in customizing products and services to meet individual needs. In trading, AI plays a significant role in algorithmic trading and portfolio management, providing quick decisions based on market trends and optimizing portfolios. Credit scoring and underwriting are also enhanced by AI, making these processes more efficient and accurate. Additionally, AI aids in risk management and fraud detection by conducting quantitative analysis and identifying market trends to detect risks and fraud. It also assists in regulatory compliance by providing accurate data for audits, ensuring banks adhere to regulatory requirements. These are just some examples of how AI is playing a significant role and will continue to do so in the banking and financial industries.
Niharika: Thank you for those insights, Mike. What are some areas of successful AI applications in banking and finance that have caught your attention recently?
Mike Vaishnav: As mentioned earlier, handling credit card applications, credit analysis, and risk assessment are prominent areas where AI has made significant strides. AI-powered chatbots and virtual assistants offer 24/7 services and automate transactions, enhancing customer service. Financial services also offer robo-advisors for portfolio management, analyzing customer behavior, and providing personalized investment recommendations. Voice recognition is another important AI application, that enhances security for logging into banking portals. AI also provides customer insights and personalized recommendations based on trends and behavior, helping banks tailor their product offerings. Algorithmic trading is another key area where AI optimizes market information and enables quick decision-making for investments and portfolio management. These applications show how AI is deeply integrated into banking and finance, driving efficiency and personalized services.
Niharika: Absolutely. However, there must be some challenges around the implementation of AI as well. What potential challenges do financial institutions typically encounter with AI solutions, and what are the strategies to overcome them?
Mike Vaishnav: One of the main challenges is data quality and accessibility. Inconsistent or outdated data can lead to incorrect decisions. This can be mitigated through data cleansing, normalization, and ensuring robust data governance and regular audits. Regulatory compliance is another challenge, given the stringent requirements in the banking industry, such as KYC (Know Your Customer), AML (Anti-Money Laundering), GDPR, and CCPA. AI can assist, but compliance teams must continuously monitor and ensure adherence to these regulations. Explainable AI techniques can provide transparency and auditability. Security and privacy concerns are also critical, as banking data requires robust cybersecurity measures, including access control and regular monitoring. Additionally, AI can sometimes perpetuate biases in data analysis, leading to ethical issues. Banks must ensure proper data audits to avoid biased outcomes. Lastly, the talent gap in AI expertise is significant. Investing in training and developing AI talent is crucial to overcome this challenge. Despite these challenges, the benefits of AI far outweigh them, and with proper monitoring and compliance, banks can effectively harness AI’s potential.
Niharika: Looking ahead, what emerging trends do you foresee shaping the future of AI in banking and finance?
Mike Vaishnav: Advanced analytics and predictive modeling will continue to evolve, enabling deeper insights into market trends and customer behavior. Conversational AI and chatbots will enhance customer service, providing 24/7 support and improving customer experience. Robotic Process Automation (RPA) will automate repetitive tasks like data entry and document management, increasing operational efficiency and cost savings. Cybersecurity and fraud detection will also advance, with AI playing a key role in safeguarding financial data. Explainable AI will provide transparency in AI-driven decisions, helping meet regulatory requirements. Overall, automation, advanced analysis, personalization, risk management, and operational efficiency will drive AI’s future in banking and finance.
Niharika: And how do you anticipate the role of AI evolving over the next decade?
Mike Vaishnav: AI will further enhance automation, advanced analytics, personalization, risk management, operational efficiency, innovation, and R&D investment. Scalability will be crucial, as AI can scale much faster than manual processes, supporting growth and efficiency in banking.
Niharika: Are there any regional or cultural differences that influence the adoption and implementation of AI in banking and finance? How does one navigate these differences in a globalized industry?
Mike Vaishnav: Yes, regional and cultural differences impact AI adoption. Regulatory environments vary by country, and technology infrastructure differs between advanced and developing nations. Cultural attitudes toward technology and AI adoption also vary, as do customer preferences. To navigate these differences, financial institutions must localize AI implementations based on specific regional requirements. This involves partnering with local stakeholders, ensuring collaboration, and customizing solutions to meet local needs. Education and training are also essential to address challenges and promote AI adoption.
Niharika: Thank you for answering that, Mike. The discussion on the role of AI in banking and finance has been quite insightful. Thank you for contributing to this conversation.
Mike Vaishnav: Thank you for having me. I’m glad to share these insights.
Moderated by Emily, Digital Transformation Consultant at Hyperbots
Emily: Hello everyone, this is Emily, a Digital Transformation Consultant at Hyperbots. I’m really pleased to have Ayo Fashina on the call with us today. Ayo is the CFO at Kobo360, and we will be discussing the transformation of finance communication and coordination tasks with vendors and customers using AI. But before we dive into the details, Ayo, could you please introduce yourself?
Ayo: Yes, thank you, Emily. It’s good to be here. My name is Ayo Fashina, and as Emily mentioned, I’m the Group CFO at Kobo360. At Kobo360, we are an e-logistics company that matches goods owners to transporters, operating in seven African countries. I oversee the finance functions for all these regions as well as our operations in the US. It’s a pleasure to be here, and I’m looking forward to our discussion on the transformation of finance communication and coordination tasks using AI.
Emily: Thank you so much, Ayo, and welcome to our discussion. To begin, I’d like to understand how AI has impacted communication and coordination tasks within the finance department of your organization.
Ayo: Thank you, Emily. AI has significantly impacted our communications and coordination tasks in various ways. As a tech-enabled logistics company, we have integrated AI to alleviate pain points inherent in logistics a traditionally manual and cumbersome sector. AI helps us track various items that might otherwise fall through the cracks, monitor payment due dates, and automate communication with customers and vendors. For instance, AI algorithms read invoices to extract key information such as due dates, amounts, and contact information. Automated notifications are sent out when invoices are issued and as they approach their due dates. This ensures our customers are reminded to make payments on time, which is crucial for our cash flow management.
Emily: Got it. What were the primary motivations behind integrating AI into your finance communication and coordination processes?
Ayo: The main objective was to ensure nothing fell through the cracks. Initially, we faced issues with delayed payments to vendors, which affected our relationships with transporters our primary vendors. We also needed to optimize our cash flow by managing payment timings more efficiently. By integrating AI, we improved our reminder system for customers and automated advance payment requests from transporters. This has significantly enhanced our cash-to-cash cycle and overall cash flow management.
Emily: Understood. How has AI specifically transformed vendor communication and coordination processes within your finance operations?
Ayo: AI has greatly enhanced our vendor management system. We can now send real-time messages to vendors about available orders, automate the matching of transporters to trips, and process advance payment requests. We also use AI to scrape information from SMS and WhatsApp to integrate customer orders into our platform, ensuring seamless coordination even when customers do not use our online portal.
Emily: Could you share specific examples where AI has significantly improved vendor interactions or transactions?
Ayo: Certainly. For example, we use AI to process advance payment requests and manage our fuel voucher system. When a transporter picks up goods, they can request an advance payment through their phone, which AI processes in real-time, considering the trip details and truck location. This automation has reduced the advance payment processing time from 24 hours to just five minutes, greatly benefiting our transporters by allowing them to refuel and continue their trips without delay.
Emily: That’s impressive. Now, what challenges did you encounter when implementing AI for vendor communication, and how did you address them?
Ayo: One major challenge was ensuring that AI could communicate through various channels, not just smartphones and emails. Many of our drivers do not have smartphones, and those that do often face battery issues on long hauls. We had to enable AI to communicate through SMS and WhatsApp to accommodate these limitations. Another challenge was ensuring the accuracy of information scraped from messages, which we addressed by refining our algorithms and ensuring thorough testing.
Emily: What lessons have you learned from overcoming these challenges?
Ayo: The key lesson is the importance of understanding the needs and limitations of your users before developing a solution. Engaging with customers and vendors to understand their pain points ensures the developed solution meets their needs. Additionally, an iterative process involving continuous feedback and improvement is crucial for success.
Emily: How has AI enhanced communication and coordination with customers in your finance department?
Ayo: AI has given us better control over our cash flow by improving visibility and tracking of invoices and payments. This has reduced our receivables days from 100 to 20 days and shortened our cash-to-cash cycle from 40 days to around 10 days. Automated reminders and notifications have also improved our collection process and reduced errors in payments.
Emily: Can you share any notable instances where AI-driven customer interactions led to improved outcomes or customer satisfaction?
Ayo: Certainly. By automating reminders and payment notifications, we’ve significantly improved our receivables days. Our transporters appreciate the timely advance payments and fuel vouchers, which have made their operations smoother. These improvements have enhanced overall customer satisfaction, as evidenced by positive feedback during focus groups and surveys.
Emily: What are the primary benefits your organization has experienced from leveraging AI in finance communication and coordination tasks?
Ayo: The primary benefits include better coordination with vendors and customers, improved payment timing, enhanced reporting accuracy, and overall improved cash flow management. These improvements have not only streamlined our operations but also contributed to our goal of achieving sustainability more quickly.
Emily: Have you observed any quantifiable improvements in efficiency, cost reduction, or other key performance indicators since implementing AI?
Ayo: Yes, we have observed significant improvements. Our cash-to-cash cycle has reduced from 40 to 10 days, and our receivables days have decreased from 100 to 20 days. Additionally, the time to process advance payments has dropped from 24 hours to five minutes. These metrics highlight the efficiency and effectiveness of our AI implementation.
Emily: How do you ensure data privacy and security while utilizing AI for communication and coordination with external parties?
Ayo: We comply with privacy standards such as GDPR and ensure sensitive data is not included in automated communications. We use AI to redact sensitive information and employ strict access controls within our platform. Only authorized personnel can access sensitive financial information, ensuring data privacy and security.
Emily: Are there any specific AI technologies or tools your organization has adopted to enhance financial communication and coordination?
Ayo: All our tools are developed in-house by our engineering team. We initially explored off-the-shelf solutions but found them inadequate for our niche requirements. Our custom-built tools are tailored to our specific needs and continuously improved based on feedback from our finance and operations teams.
Emily: What metrics or key performance indicators do you use to measure the effectiveness of AI-driven communication within the finance department?
Ayo: We track accuracy and effectiveness of communication, read and open rates of messages, delivery time of reports, and improvements in financial metrics such as receivables and payable days. These indicators help us evaluate the success of our AI implementation and identify areas for further optimization.
Emily: Have you identified any areas for further optimization or refinement based on these performance metrics?
Ayo: Yes, we are continuously optimizing our tools based on feedback and performance metrics. Currently, we are exploring the use of chatbots to handle standardized queries from vendors, which would further enhance efficiency and reduce the need for human intervention.
Emily: How do you envision the future of AI in transforming finance communication and coordination tasks?
Ayo: I see AI enabling more personalized and empathetic communication, leveraging advanced algorithms and machine learning to foster meaningful connections. AI will continue to evolve, offering more nuanced responses and insights, ultimately transforming finance operations to be more efficient and customer-centric.
Emily: What emerging trends or developments in AI technology do you believe will have a significant impact on finance operations in the coming years?
Ayo: Data-driven processes will become more strategic, and virtual assistants and chatbots will be more mainstream, providing 24/7 support. AI assistants will handle more complex issues and connect with customers in a natural manner, reducing the need for a large customer service team and improving overall efficiency.
Emily: Thank you, Ayo, for sharing such valuable insights. It’s been a pleasure discussing the transformation of finance communication and coordination tasks with AI. Any final thoughts?
Ayo: Thank you, Emily. It’s been a great discussion. I believe AI will continue to play a crucial role in enhancing finance operations, and I’m excited to see how these technologies evolve to meet the ever-changing needs of our industry.
Emily: Thank you, Ayo. And thank you, everyone, for joining us today. Until next time, stay tuned for more insights on digital transformation.
Conversational UX is gaining traction in tandem with rapid advancement in AI tech. It seems intuitive that humans would want to communicate with AI agents or bots as naturally as possible. Nothing about conversational UX is new, of course. We just happen to be at a tipping point where various AI technology trends are pushing it into prominence. Substantial research and successful application of that research for real-world scenarios over the past decade have made conversational UX ubiquitous and ready for primetime where the best is yet to come.
The new age of sophisticated applications using generative AI demands that designers dig deep into the art of conversation. This is an exciting time to explore possibilities to make a dialogue between humans and bots, natural, meaningful, fun, and engaging. On the B2B SaaS front, we are just scratching the surface.
With AI infused in all kinds of finance process automation, there are a ton of possibilities to make conversational UX a key part of such applications. It begs a radical question. What if there is no traditional UI layer in finance applications? Can business outcomes be achieved through good old easy-going conversations between humans and AI solely through a chat window with no regular app interface to speak of? How can designers design and orchestrate the creation of these environments? Designers must investigate, for instance, whats the equivalent of a casual business meeting in a cafe versus a mission-critical exchange in a conference room about budgeting between a CFO and their AI assistant.
At Hyperbots, designers are exploring ways to create a humane, relatable avatar for the powerful AI capabilities of our platform addressing the automation needs of finance processes like Accounts Payable and Expense Processing for the CFOs office processes that are still woefully manual.
The core work that the Hyperbots AI Assistants do are:
Accountant queries can be as practical as asking the AI assistant about invoices that can be safely bulk-approved or the ones that need their manual review.
The AI assistant reponds with real-time actionable data about pending invoices that need manual review.
Analytics critical to business decision making can be easily pulled up with a simple query.
These are sneak peeks into the early work that’s emerging as part of the conversational UX design charter for the design team at Hyperbots. Within broad chatbot categories that exist today, here’s where we might fit in.
Designers at Hyperbots know that if they want to create distinct AI Assistant identities, they need to focus beyond the visual elements of an avatar or the UI layer of a dialogue box. They must ask the question what makes a dialogue meaningful? Especially between a machine and a human. They must dive deep into the science of Human-Computer Interaction and the art of conversation. So far our secondary design research has pointed to some seminal work already in the public domain like the recent ethnographic study by NNGroup into usage patterns of ChatGPT, Bing, and Bard users suggesting there could be 6 different types of conversations with generative AI.
These provide a great basis for brushing up on fundamentals and taking the right first step. What should follow is arriving at a solid hypothesis of what specific approaches might work for ur CFOs and their teams and then testing these hypotheses with rigor.
We are early in our exploration of conversational UX at Hyperbots. We are more than convinced this space cannot remain untapped if we are to create a groundbreaking experience for our customers grappling with legacy applications to conduct their finance operations central to the customer experience we want to build for our CFOs and their teams. As they say, watch this space!