SBV committed to efficiency through digitalisation

Tran Thi Phuong Hong, general director, TechX

“Challenges in building a data strategy”

In an age where technology equips us with the tools to manage vast volumes of data, the real challenges faced by the banking sector originate from the human element, internal processes, and the dynamics of change management. Banks must not only acquire expertise in big data platforms and data science but also foster a profound transformation in their organizational culture and perception.

Leaders, starting from the upper echelons and extending throughout the ranks, need to appreciate how data has the potential to revolutionize a bank's business model and lead it to success. Implementing big data solutions necessitates the development of a clearly defined data collection and enrichment strategy that aligns closely with the bank's specific business challenges. Moreover, it is crucial to address security and compliance requirements, particularly when deploying big data solutions on cloud platforms to harness processing power and integration tools.

The aspirations of banks in their journey of digital transformation, firstly, banks are striving to offer a personalized and exceptional customer service experience by placing the customer at the heart of their operations. Data analytics emerges as the pivotal element in this endeavor, enabling banks to understand customer needs, preferences, and real-time behavior. This comprehensive understanding of the customer journey empowers banks to track interactions, analyze experiences, and proactively respond to evolving customer needs.

In addition, to streamline the sales process, banks are identifying cross-selling opportunities based on customer profiles and transaction history. Sales automation, driven by AI-powered chatbots and automated marketing campaigns, liberates sales personnel to concentrate on high-value interactions with customers.

Besides that, banking institutions are leveraging data collected from transaction history and social networks to build comprehensive customer profiles. This data serves as the foundation for analyzing customer psychology and preferences, thereby facilitating tailored marketing campaigns with higher response rates.

Lastly, Big Data algorithms come into play to fortify risk management and compliance with legal regulations while simultaneously reducing the risk of fraud. Furthermore, data analysis contributes to the enhancement of accounting, auditing, and financial reporting processes.

Nguyen Chien Thang, director, BIDV Digital Banking Centre

“Is GenAI a novice or a premier customer care specialist?”

In the past year, generative AI (GenAI) has surged into public consciousness. Within six months, ChatGPT attracted over 100 million users, while OpenAI’s DALL·E 2 garnered a staggering 4.2 billion, leading to the emergence of rivals like Google’s Bard.

Industry analysts Gardner point out that GenAI is on track to become as transformative as historical breakthroughs such as the steam engine, electricity, and the internet. Although initial enthusiasm might wane as implementation challenges become apparent, the influence of GenAI is set to grow. Individuals and businesses alike are bound to discover an array of innovative applications that will reshape daily work and life.

GenAI’s emergence is leaving an indelible mark on many IT areas, most notably the realm of customer experience. Machines are now approaching human-like communication abilities. This progress in GenAI brings about the opportunity to tap into vast data reservoirs in previously unimaginable ways. However, it’s vital to provide these systems with access to pertinent business data.

There are three core avenues for this. Firstly, training is paramount. Much like a novice employee, GenAI must undergo rigorous training with ample business data, encompassing all customer service aspects. Just as humans don’t excel without education and hands-on experience, the same principle applies to GenAI.

Secondly, the journey doesn’t end at initial training. Continual refinement is a must. The art of customer service requires persistent model tweaking to reach the expected sophistication level. It’s the intricate details and unique organisational nuances that guide GenAI to become truly proficient and insightful.

Lastly, the technique of suggestion engineering plays a pivotal role in GenAI’s evolution. Offering continuous feedback or suggestions helps the technology become more astute, better aligned with an organisation’s unique requirements, ensuring it adheres to set policies and objectives.

While the promise of GenAI in elevating customer service is vast, it’s not without risks. There’s the possibility of it delivering incorrect information, inadvertently fostering biases that could result in unfair treatment, and concerns over data security.

Phillip L. Wright, COO, HSBC Vietnam

“The power of AI in banking”

AI enables banks to improve the services we provide to customers, and helps them manage their business more efficiently and effectively. How banks analyse data is a great example – they can understand their customers better and personalise how they interact with them, including how banks protect them from fraud.

We’re highly aware of the potential risks of AI, so have established a clear set of ethics considerations and principles. Maintaining our customers’ trust is of upmost importance. We are focused on ensuring we carefully manage and protect data, are transparent about how we use it, and understand the risks associated with it.

We are already using AI widely to do things like improve customer service and increase the efficiency of our processes. To further explore the potential, we’re developing a dedicated innovation team to explore emerging capabilities in generative AI, synthetic data, and advanced AI models. And we’re conducting research with world-leading academic institutions and technology companies. Finally, we’re leveraging our scale and developing a group-wide AI strategy that will help us deliver the next generation of digital banking.

Examples of how we’re using AI include scanning for potential signs of financial crime far more effectively, and providing institutional investors with a digital service offering that uses purpose-built natural language processing to enrich the way they interact with global markets.

We also created a system, in partnership with Google, that monitors contact centre calls and checks if call agents have sold a specific product correctly by explaining the terms and conditions in the correct way.

In particular, we’re using the latest AI and cloud technology to help identify suspicious activity and prevent financial crime in several markets. Our dynamic risk assessment solution analyses and identifies criminal activity, enabling us to spot genuine crimes twice as fast and with much greater accuracy, generating 60 per cent fewer false cases than previously.

Google Cloud has launched the anti-money laundering AI solution – which we co-developed – that could transform how financial crime is tackled across the industry. It has helped HSBC improve detection capability, deliver more accurate results, and significantly reduce batch processing times for its large customer base.

Le Phuong Hai, deputy general director, VietCredit

“Emergent non-contact models revolutionising consumer credit in Vietnam”

In the rapidly evolving world of e-commerce and consumer credit, non-contact identification has ushered in a transformative era. Beyond just mitigating costs of customer engagement, this technology has deftly addressed geographical constraints.

Lenders no longer feel compelled to expand their network of service introduction points, branches, transaction rooms, or representative offices. This technological evolution now ensures that consumer credit services reach even the most remote areas, including rural locales, mountainous regions, and islands.

In traditional engagement models, interactions between customers and financial institutions were limited to a few touchpoints, such as branches, transaction rooms, and service introduction points. The non-contact model, on the other hand, offers an infinite array of touchpoints, spanning e-commerce platforms, social networks, over-the-top services, and user forums.

In the digital sphere, consumers seeking credit seemingly encounter financial companies at every turn. Conversely, data allows in-depth user behaviour studies, enabling lenders to reach new potential customers with latent borrowing needs. In essence, these interactions are more targeted, translating to substantial savings in marketing expenses, customer acquisition, and product promotion.

Under the traditional model, the customer acquisition cost ranged around $40-125 per customer. If this were assessed relative to the loan’s outstanding balance, representing 5-10 per cent, the adoption of digital footprint-based prediction models could reduce these costs by 70-80 per cent. This translates to a mere $13-21 per customer.

Some financial institutions are even laying the groundwork for a Zero-CAC model, where the cost of acquiring a customer is virtually nil. Such cost reductions can set the stage for interest rate cuts, thereby attracting a larger pool of potential borrowers.

After acquiring customers, however, some institutions have historically overlooked post-lending customer care. It is partly because they failed to see optimisation potential in their loan portfolio and partly due to the relatively high post-lending data collection costs.

Enhanced product offerings enable credit institutions to periodically query the credit status of their clients from other lending organisations more aggressively. This provides options for clients and tailored customer management techniques, minimising bad debts.