Tag: data analytics

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 Many banks still rely on outdated core systems, hindering agility, innovation, and personalised customer experiences.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 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.

Setting and achieving Sustainability goals is complex in BFSI. To be truly sustainable, organisations need to:​

• Reduce internal energy consumption and carbon footprint​
• Fund the transition to decarbonisation in high emission industries​
• Introduce “green” customer​ products and services 
• Monitor carbon data for financed emissions​​

Data and AI have the potential to assist in achieving these objectives, provided they are used effectively.​ Here is how.

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The challenge of AI is that it is hard to build a business case when the outcomes are inherently uncertain. Unlike a traditional process improvement procedure, there are few guarantees that AI will solve the problem it is meant to solve. Organisations that have been experimenting with AI for some time are aware of this, and have begun to formalise their Proof of Concept (PoC) process to make it easily repeatable by anyone in the organisation who has a use case for AI. PoCs can validate assumptions, demonstrate the feasibility of an idea, and rally stakeholders behind the project.

PoCs are particularly useful at a time when AI is experiencing both heightened visibility and increased scrutiny. Boards, senior management, risk, legal and cybersecurity professionals are all scrutinising AI initiatives more closely to ensure they do not put the organisation at risk of breaking laws and regulations or damaging customer or supplier relationships.

13 Steps to Building an AI PoC

Despite seeming to be lightweight and easy to implement, a good PoC is actually methodologically sound and consistent in its approach. To implement a PoC for AI initiatives, organisations need to:

• Clearly define the problem. Businesses need to understand and clearly articulate the problem they want AI to solve. Is it about improving customer service, automating manual processes, enhancing product recommendations, or predicting machinery failure?
• Set clear objectives. What will success look like for the PoC? Is it about demonstrating technical feasibility, showing business value, or both? Set tangible metrics to evaluate the success of the PoC.
• Limit the scope. PoCs should be time-bound and narrow in scope. Instead of trying to tackle a broad problem, focus on a specific use case or a subset of data.
• Choose the right data. AI is heavily dependent on data. For a PoC, select a representative dataset that’s large enough to provide meaningful results but manageable within the constraints of the PoC.
• Build a multidisciplinary team. Involve team members from IT, data science, business units, and other relevant stakeholders. Their combined perspectives will ensure both technical and business feasibility.
• Prioritise speed over perfection. Use available tools and platforms to expedite the development process. It’s more important to quickly test assumptions than to build a highly polished solution.
• Document assumptions and limitations. Clearly state any assumptions made during the PoC, as well as known limitations. This helps set expectations and can guide future work.
• Present results clearly. Once the PoC is complete, create a clear and concise report or presentation that showcases the results, methodologies, and potential implications for the business.
• Get feedback. Allow stakeholders to provide feedback on the PoC. This includes end-users, technical teams, and business leaders. Their insights will help refine the approach and guide future iterations.
• Plan for the next steps. What actions need to follow a successful PoC demonstration? This might involve a pilot project with a larger scope, integrating the AI solution into existing systems, or scaling the solution across the organisation.
• Assess costs and ROI. Evaluate the costs associated with scaling the solution and compare it with the anticipated ROI. This will be crucial for securing budget and support for further expansion.
• Continually learn and iterate. AI is an evolving field. Use the PoC as a learning experience and be prepared to continually iterate on your solutions as technologies and business needs evolve.
• Consider ethical and social implications. Ensure that the AI initiative respects privacy, reduces bias, and upholds the ethical standards of the organisation. This is critical for building trust and ensuring long-term success.

Customising AI for Your Business

The primary purpose of a PoC is to validate an idea quickly and with minimal risk. It should provide a clear path for decision-makers to either proceed with a more comprehensive implementation or to pivot and explore alternative solutions. It is important for the legal, risk and cybersecurity teams to be aware of the outcomes and support further implementation.

AI initiatives will inevitably drive significant productivity and customer experience improvements – but not every solution will be right for the business. At Ecosystm, we have come across organisations that have employed conversational AI in their contact centres to achieve entirely distinct results – so the AI experience of peers and competitors may not be relevant. A consistent PoC process that trains business and technology teams across the organisation and encourages experimentation at every possible opportunity, would be far more useful.

The impact of AI on Customer Experience (CX) has been profound and continues to expand. AI allows a a range of advantages, including improved operational efficiency, cost savings, and enhanced experiences for both customers and employees.

AI-powered solutions have the capability to analyse vast volumes of customer data in real-time, providing organisations with invaluable insights into individual preferences and behaviour. When executed effectively, the ability to capture, analyse, and leverage customer data at scale gives organisations significant competitive edge. Most importantly, AI unlocks opportunities for innovation.

Read on to discover the transformative impact of AI on customer experiences.

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Technology is reshaping the Public Sector worldwide, optimising operations, improving citizen services, and fostering data-driven decision-making. Government agencies are also embracing innovation for effective governance in this digital era.​

Public sector organisations worldwide recognise the need for swift and agile interventions. With citizen expectations resembling those of commercial customers, public sector organisations face mounting pressure to break down the barriers to provide seamless service experiences.​

AI has become intrinsic to our personal lives – we are often completely unaware of technology’s influence on our daily lives. For enterprises too, tech solutions often come embedded with AI capabilities. Today, an organisation’s ability to automate processes and decisions is often dependent more on their desire and appetite for tech adoption, than the technology itself. 

In 2022 the key focus for enterprises will be on being able to trust their Data & AI solutions. This will include trust in their IT infrastructure, architecture and AI services; and stretch to being able to participate in trusted data sharing models. Technology vendors will lead this discussion and showcase their solutions in the light of trust.  

2020 was a strange year for retail. Businesses witnessed significant disruption to supply chains, significant swings in demand for products (toilet paper, puzzles, bikes etc!) and then sometimes incredible growth – as disposable income increased as many consumers are no longer taking expensive holidays. Overall, it was a mixed year, with many retailers closing down and others reporting record sales. The grocery sector boomed – with many restaurants and fast-food providers closed, sometimes the supermarkets were some of the few remaining open retailers.

For many retailers, technology has become a key enabler to their transformation, survival and success (Figure 1).