Technological innovation is dramatically changing how organisations interact with modern consumers in the rapidly evolving banking, financial services, and insurance (BFSI) industry. The growing dependence on digital communication tools and platforms lies at the core of this transformation. These tools have become vital for BFSI organisations to meet the dynamic needs of today’s customers, enabling agile, responsive Sales & Support teams that can use real-time data to sustain customer engagement, ensure data security, comply with regulations, and streamline operations.

Customer Engagement Challenges in BFSI Organisations

Security Concerns. Customers in the BFSI industry are increasingly concerned about the security of their financial transactions and Personal Identifiable Information (PII). With the rise of cyber threats, customers expect robust security measures to protect their accounts and sensitive information. BFSI organisations need to continually invest in cybersecurity infrastructure and technologies to reassure customers and maintain their trust.

Customer Expectations. In the competitive landscape of the BFSI industry, customer retention and attraction are critical to sustaining profitability. Organisations must prioritise an agile approach that adapts swiftly to market changes. Central to this strategy is the delivery of personalised experiences aligned with individual preferences and needs, driven by advancements in digitalisation. To achieve this, BFSI organisations have to increase investments in AI-driven solutions to gain deep insights into customer behaviour, enabling them to accurately anticipate and meet evolving needs.

Regulatory Compliance. The industry operates in a highly regulated environment with strict compliance requirements imposed by various regulatory bodies. Ensuring compliance with constantly evolving regulations such as GDPR, PSD2, Dodd-Frank, etc., poses a significant challenge for organisations. To complicate the landscape further, institutions with cross-border operations need to consider the laws in different countries. Compliance efforts often result in additional operational complexities and costs, which can impact the overall customer experience if not managed effectively.

Digital Transformation. Rapid technological advancements and changing customer preferences are driving BFSI organisations to undergo digital transformation initiatives. However, legacy systems and processes hinder their ability to innovate and adapt to digital trends quickly. Transitioning to modern, agile architectures while ensuring uninterrupted services and minimal disruption to customers is a complex undertaking for many BFSI organisations.

Customer Education and Communication. Financial products and services can be complex, and customers often require guidance to make informed decisions. Sales & Support teams in BFSI organisations struggle to effectively educate their customers about the features, benefits, and risks associated with various products. Clear and transparent communication regarding fees, terms, and conditions is essential for building trust and maintaining customer satisfaction. Balancing regulatory requirements with the need for transparent communication can be challenging.

5 Ways to Empower Sales & Support Teams in BFSI

BFSI organisations in Asia Pacific often overlook technology enablement for the empowerment of their Sales & Support and other customer engagement teams. Key measures to empower these teams include upskilling for role flexibility and offering competitive remuneration for better employee retention.

Organisations should prioritise upgrading Sales & Support tools and solutions to address the team’s key pain points.

#1 Boost Customer Engagement with Omnichannel Support

BFSI organisations need to work on a suite of API-driven solutions to create a comprehensive omnichannel presence. This enables engagement with customers via their preferred channels, such as SMS, email, voice, chat, or video. Such flexibility enhances customer satisfaction and loyalty by ensuring personalised and convenient interactions. This includes capabilities such as the ability to deploy messaging and voice services to dispatch timely account activity alerts, secure transactions with two-factor authentication, and deliver customised financial advice through chatbots or direct communications.

#2 Streamline Customer Service with AI and Virtual Assistants

Integrating AI and virtual assistants allows BFSI companies to automate standard inquiries and transactions, freeing Sales & Support teams to tackle more sophisticated customer needs. These AI tools can interpret and process natural language, facilitating conversational interactions with automated services. This boosts efficiency and shortens response times, elevating the customer engagement experience. Also, consistently integrating these virtual assistants across various channels ensures a uniform customer experience – and brand image.

#3 Enhance Security Measures and Compliance Standards

Adhering to stringent security and compliance requirements is essential for BFSI organisations. A secure platform complies with critical global and country-level standards and regulations. The voice and video communication services must include comprehensive encryption, protecting all customer interactions. There is also a need to have a suite of tools for monitoring and auditing communications to meet compliance requirements, allowing BFSI organisations to protect sensitive data while providing secure communication options.

#4 Leverage Insights for Personalised Customer Interactions

BFSI organisations must focus on aggregating, harmonising, and scrutinising customer interactions across various channels. This holistic view of customer behaviour allows for more targeted and personalised services, enhancing customer engagement and loyalty. By leveraging insights into customers’ interaction histories, preferences, and financial objectives, companies can customise their outreach and recommendations, improving upselling, cross-selling, and retention strategies.

#5 Increase Operational Efficiency with Cloud-Based Solutions

Cloud-based communication solutions offer BFSI organisations the scalability and flexibility needed to respond swiftly to market shifts and customer demands. This adaptability is vital for fostering growth in a dynamic industry. A customisable solution supports organisations in refining their operations, from automating workflows to integrating CRM systems, enabling Sales & Support teams to operate more smoothly and effectively. Cloud technology helps reduce operational expenses, elevate service quality, and spur innovation.

Digital communication and collaboration tools have the power to revolutionise BFSI, enhancing engagement, security, and efficiency. Through APIs, AI, and cloud, organisations can meet evolving market needs, driving growth and innovation. Embracing these solutions ensures competitiveness and agility in a changing landscape.

Databases are foundational elements in the tech ecosystem, crucial for managing various data types efficiently. Beyond the traditional relational and NoSQL databases, specialised databases like Time-Series, Spatial, and Document-oriented databases cater to specific needs, enhancing data processing and analysis capabilities. This Ecosystm Insights discusses database categories, offering insights into their functionalities and examples of vendors and products.

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Healthcare delivery and healthtech have made significant strides; yet, the fundamental challenges in healthcare have remained largely unchanged for decades. The widespread acceptance and integration of digital solutions in recent years have supported healthcare providers’ primary goals of enhancing operational efficiency, better resource utilisation (with addressing skill shortages being a key driver), improving patient experience, and achieving better clinical outcomes. With governments pushing for advancements in healthcare outcomes at sustainable costs, the concept of value-based healthcare has gained traction across the industry.

Technology-driven Disruption

As AI evolves rapidly, the emergence of GenAI technologies such as GPT models has sparked a novel and critical role: prompt engineering. This specialised function is becoming indispensable in optimising the interaction between humans and AI, serving as a bridge that translates human intentions into prompts that guide AI to produce desired outcomes. In this Ecosystm Insight, I will explore the importance of prompt engineering, highlighting its significance, responsibilities, and the impact it has on harnessing AI’s full potential.

Understanding Prompt Engineering

Prompt engineering is an interdisciplinary role that combines elements of linguistics, psychology, computer science, and creative writing. It involves crafting inputs (prompts) that are specifically designed to elicit the most accurate, relevant, and contextually appropriate responses from AI models. This process requires a nuanced understanding of how different models process information, as well as creativity and strategic thinking to manipulate these inputs for optimal results.

As GenAI applications become more integrated across sectors – ranging from creative industries to technical fields – the ability to effectively communicate with AI systems has become a cornerstone of leveraging AI capabilities. Prompt engineers play a crucial role in this scenario, refining the way we interact with AI to enhance productivity, foster innovation, and create solutions that were previously unimaginable.

The Art and Science of Crafting Prompts

Prompt engineering is as much an art as it is a science. It demands a balance between technical understanding of AI models and the creative flair to engage these models in producing novel content. A well-crafted prompt can be the difference between an AI generating generic, irrelevant content and producing work that is insightful, innovative, and tailored to specific needs.

Key responsibilities in prompt engineering include:

• Prompt Optimisation. Fine-tuning prompts to achieve the highest quality output from AI models. This involves understanding the intricacies of model behaviour and leveraging this knowledge to guide the AI towards desired responses.
• Performance Testing and Iteration. Continuously evaluating the effectiveness of different prompts through systematic testing, analysing outcomes, and refining strategies based on empirical data.
• Cross-Functional Collaboration. Engaging with a diverse team of professionals, including data scientists, AI researchers, and domain experts, to ensure that prompts are aligned with project goals and leverage domain-specific knowledge effectively.
• Documentation and Knowledge Sharing. Developing comprehensive guidelines, best practices, and training materials to standardise prompt engineering methodologies within an organisation, facilitating knowledge transfer and consistency in AI interactions.

The Strategic Importance of Prompt Engineering

Effective prompt engineering can significantly enhance the efficiency and outcomes of AI projects. By reducing the need for extensive trial and error, prompt engineers help streamline the development process, saving time and resources. Moreover, their work is vital in mitigating biases and errors in AI-generated content, contributing to the development of responsible and ethical AI solutions.

As AI technologies continue to advance, the role of the prompt engineer will evolve, incorporating new insights from research and practice. The ability to dynamically interact with AI, guiding its creative and analytical processes through precisely engineered prompts, will be a key differentiator in the success of AI applications across industries.

Want to Hire a Prompt Engineer?

Here is a sample job description for a prompt engineer if you think that your organisation will benefit from the role.

Conclusion

Prompt engineering represents a crucial evolution in the field of AI, addressing the gap between human intention and machine-generated output. As we continue to explore the boundaries of what AI can achieve, the demand for skilled prompt engineers – who can navigate the complex interplay between technology and human language – will grow. Their work not only enhances the practical applications of AI but also pushes the frontier of human-machine collaboration, making them indispensable in the modern AI ecosystem.

Banks, insurers, and other financial services organisations in Asia Pacific have plenty of tech challenges and opportunities including cybersecurity and data privacy management; adapting to tech and customer demands, AI and ML integration; use of big data for personalisation; and regulatory compliance across business functions and transformation journeys.

Modernisation Projects are Back on the Table

An emerging tech challenge lies in modernising, replacing, or retiring legacy platforms and systems. Many banks still rely on outdated core systems, hindering agility, innovation, and personalised customer experiences. Migrating to modern, cloud-based systems presents challenges due to complexity, cost, and potential disruptions. Insurers are evaluating key platforms amid evolving customer needs and business models; ERP and HCM systems are up for renewal; data warehouses are transforming for the AI era; even CRM and other CX platforms are being modernised as older customer data stores and models become obsolete.

For the past five years, many financial services organisations in the region have sidelined large legacy modernisation projects, opting instead to make incremental transformations around their core systems. However, it is becoming critical for them to take action to secure their long-term survival and success.

Benefits of legacy modernisation include:

• Improved operational efficiency and agility
• Enhanced customer experience and satisfaction
• Increased innovation and competitive advantage
• Reduced security risks and compliance costs
• Preparation for future technologies

AI has become a business necessity today, catalysing innovation, efficiency, and growth by transforming extensive data into actionable insights, automating tasks, improving decision-making, boosting productivity, and enabling the creation of new products and services.

Generative AI stole the limelight in 2023 given its remarkable advancements and potential to automate various cognitive processes. However, now the real opportunity lies in leveraging this increased focus and attention to shine the AI lens on all business processes and capabilities. As organisations grasp the potential for productivity enhancements, accelerated operations, improved customer outcomes, and enhanced business performance, investment in AI capabilities is expected to surge.

Over the past year, many organisations have explored Generative AI and LLMs, with some successfully identifying, piloting, and integrating suitable use cases. As business leaders push tech teams to implement additional use cases, the repercussions on their roles will become more pronounced. Embracing GenAI will require a mindset reorientation, and tech leaders will see substantial impact across various ‘traditional’ domains.

AIOps and GenAI Synergy: Shaping the Future of IT Operations

When discussing AIOps adoption, there are commonly two responses: “Show me what you’ve got” or “We already have a team of Data Scientists building models”. The former usually demonstrates executive sponsorship without a specific business case, resulting in a lukewarm response to many pre-built AIOps solutions due to their lack of a defined business problem. On the other hand, organisations with dedicated Data Scientist teams face a different challenge. While these teams can create impressive models, they often face pushback from the business as the solutions may not often address operational or business needs. The challenge arises from Data Scientists’ limited understanding of the data, hindering the development of use cases that effectively align with business needs.

The most effective approach lies in adopting an AIOps Framework. Incorporating GenAI into AIOps frameworks can enhance their effectiveness, enabling improved automation, intelligent decision-making, and streamlined operational processes within IT operations.

This allows active business involvement in defining and validating use-cases, while enabling Data Scientists to focus on model building. It bridges the gap between technical expertise and business requirements, ensuring AIOps initiatives are influenced by the capabilities of GenAI, address specific operational challenges and resonate with the organisation’s goals.

The Next Frontier of IT Infrastructure

Many companies adopting GenAI are openly evaluating public cloud-based solutions like ChatGPT or Microsoft Copilot against on-premises alternatives, grappling with the trade-offs between scalability and convenience versus control and data security.

Cloud-based GenAI offers easy access to computing resources without substantial upfront investments. However, companies face challenges in relinquishing control over training data, potentially leading to inaccurate results or “AI hallucinations,” and concerns about exposing confidential data. On-premises GenAI solutions provide greater control, customisation, and enhanced data security, ensuring data privacy, but require significant hardware investments due to unexpectedly high GPU demands during both the training and inferencing stages of AI models.

Hardware companies are focusing on innovating and enhancing their offerings to meet the increasing demands of GenAI. The evolution and availability of powerful and scalable GPU-centric hardware solutions are essential for organisations to effectively adopt on-premises deployments, enabling them to access the necessary computational resources to fully unleash the potential of GenAI. Collaboration between hardware development and AI innovation is crucial for maximising the benefits of GenAI and ensuring that the hardware infrastructure can adequately support the computational demands required for widespread adoption across diverse industries. Innovations in hardware architecture, such as neuromorphic computing and quantum computing, hold promise in addressing the complex computing requirements of advanced AI models.

The synchronisation between hardware innovation and GenAI demands will require technology leaders to re-skill themselves on what they have done for years – infrastructure management.

The Rise of Event-Driven Designs in IT Architecture

IT leaders traditionally relied on three-tier architectures – presentation for user interface, application for logic and processing, and data for storage. Despite their structured approach, these architectures often lacked scalability and real-time responsiveness. The advent of microservices, containerisation, and serverless computing facilitated event-driven designs, enabling dynamic responses to real-time events, and enhancing agility and scalability. Event-driven designs, are a paradigm shift away from traditional approaches, decoupling components and using events as a central communication mechanism. User actions, system notifications, or data updates trigger actions across distributed services, adding flexibility to the system.

However, adopting event-driven designs presents challenges, particularly in higher transaction-driven workloads where the speed of serverless function calls can significantly impact architectural design. While serverless computing offers scalability and flexibility, the latency introduced by initiating and executing serverless functions may pose challenges for systems that demand rapid, real-time responses. Increasing reliance on event-driven architectures underscores the need for advancements in hardware and compute power. Transitioning from legacy architectures can also be complex and may require a phased approach, with cultural shifts demanding adjustments and comprehensive training initiatives.  

The shift to event-driven designs challenges IT Architects, whose traditional roles involved designing, planning, and overseeing complex systems. With Gen AI and automation enhancing design tasks, Architects will need to transition to more strategic and visionary roles. Gen AI showcases capabilities in pattern recognition, predictive analytics, and automated decision-making, promoting a symbiotic relationship with human expertise. This evolution doesn’t replace Architects but signifies a shift toward collaboration with AI-driven insights.

IT Architects need to evolve their skill set, blending technical expertise with strategic thinking and collaboration. This changing role will drive innovation, creating resilient, scalable, and responsive systems to meet the dynamic demands of the digital age.

Whether your organisation is evaluating or implementing GenAI, the need to upskill your tech team remains imperative. The evolution of AI technologies has disrupted the tech industry, impacting people in tech. Now is the opportune moment to acquire new skills and adapt tech roles to leverage the potential of GenAI rather than being disrupted by it.

“AI Guardrails” are often used as a method to not only get AI programs on track, but also as a way to accelerate AI investments. Projects and programs that fall within the guardrails should be easy to approve, govern, and manage – whereas those outside of the guardrails require further review by a governance team or approval body. The concept of guardrails is familiar to many tech businesses and are often applied in areas such as cybersecurity, digital initiatives, data analytics, governance, and management.

While guidance on implementing guardrails is common, organisations often leave the task of defining their specifics, including their components and functionalities, to their AI and data teams. To assist with this, Ecosystm has surveyed some leading AI users among our customers to get their insights on the guardrails that can provide added value.

Data Security, Governance, and Bias

• Data Assurance. Has the organisation implemented robust data collection and processing procedures to ensure data accuracy, completeness, and relevance for the purpose of the AI model? This includes addressing issues like missing values, inconsistencies, and outliers.
• Bias Analysis. Does the organisation analyse training data for potential biases – demographic, cultural and so on – that could lead to unfair or discriminatory outputs?
• Bias Mitigation. Is the organisation implementing techniques like debiasing algorithms and diverse data augmentation to mitigate bias in model training?
• Data Security. Does the organisation use strong data security measures to protect sensitive information used in training and running AI models?
• Privacy Compliance. Is the AI opportunity compliant with relevant data privacy regulations (country and industry-specific as well as international standards) when collecting, storing, and utilising data?

Model Development and Explainability

• Explainable AI. Does the model use explainable AI (XAI) techniques to understand and explain how AI models reach their decisions, fostering trust and transparency?
• Fair Algorithms. Are algorithms and models designed with fairness in mind, considering factors like equal opportunity and non-discrimination?
• Rigorous Testing. Does the organisation conduct thorough testing and validation of AI models before deployment, ensuring they perform as intended, are robust to unexpected inputs, and avoid generating harmful outputs?

AI Deployment and Monitoring

• Oversight Accountability. Has the organisation established clear roles and responsibilities for human oversight throughout the AI lifecycle, ensuring human control over critical decisions and mitigation of potential harm?
• Continuous Monitoring. Are there mechanisms to continuously monitor AI systems for performance, bias drift, and unintended consequences, addressing any issues promptly?
• Robust Safety. Can the organisation ensure AI systems are robust and safe, able to handle errors or unexpected situations without causing harm? This includes thorough testing and validation of AI models under diverse conditions before deployment.
• Transparency Disclosure. Is the organisation transparent with stakeholders about AI use, including its limitations, potential risks, and how decisions made by the system are reached?

Other AI Considerations

• Ethical Guidelines. Has the organisation developed and adhered to ethical principles for AI development and use, considering areas like privacy, fairness, accountability, and transparency?
• Legal Compliance. Has the organisation created mechanisms to stay updated on and compliant with relevant legal and regulatory frameworks governing AI development and deployment?
• Public Engagement. What mechanisms are there in place to encourage open discussion and engage with the public regarding the use of AI, addressing concerns and building trust?
• Social Responsibility. Has the organisation considered the environmental and social impact of AI systems, including energy consumption, ecological footprint, and potential societal consequences?

Implementing these guardrails requires a comprehensive approach that includes policy formulation, technical measures, and ongoing oversight. It might take a little longer to set up this capability, but in the mid to longer term, it will allow organisations to accelerate AI implementations and drive a culture of responsible AI use and deployment.

For many organisations migrating to cloud, the opportunity to run workloads from energy-efficient cloud data centres is a significant advantage. However, carbon emissions can vary from one country to another and if left unmonitored, will gradually increase over time as cloud use grows. This issue will become increasingly important as we move into the era of compute-intensive AI and the burden of cloud on natural resources will shift further into the spotlight.