Consider the sheer volume of information flowing through today’s financial systems: every QR payment, e-KYC onboarding, credit card swipe, and cross-border transfer captures a data point. With digital banking and Open Banking, financial institutions are sitting on a goldmine of insights. But this isn’t just about data collection; it’s about converting that data into strategic advantage in a fast-moving, customer-driven landscape.
With digital banks gaining traction and regulators around the world pushing bold reforms, the industry is entering a new phase of financial innovation powered by data and accelerated by AI.
Ecosystm gathered insights and identified key challenges from senior banking leaders during a series of roundtables we moderated across Asia Pacific. The conversations revealed a clear picture of where momentum is building – and where obstacles continue to slow progress. From these discussions, several key themes emerged that highlight both opportunities and ongoing barriers in the Banking sector.
1. AI is Leading to End-to-End Transformation
Banks are moving beyond generic digital offerings to deliver hyper-personalised, data-driven experiences that build loyalty and drive engagement. AI is driving this shift by helping institutions anticipate customer needs through real-time analysis of behavioural, transactional, and demographic data. From pre-approved credit offers and contextual investment nudges to app interfaces that adapt to individual financial habits, personalisation is becoming a core strategy, not just a feature. This is a huge departure from reactive service models, positioning data as a long-term strategic asset.
But the impact of AI isn’t limited to customer-facing experiences. It’s also driving innovation deep within the banking stack, from fraud detection and SME loan processing to intelligent chatbots that scale customer support. On the infrastructure side, banks are investing in agile, AI-ready platforms to support automation, model training, and advanced analytics at scale. These shifts are redefining how banks operate, make decisions, and deliver value. Institutions that integrate AI across both front-end journeys and back-end processes are setting a new benchmark for agility, efficiency, and competitiveness in a fast-changing financial landscape.
2. Regulatory Shifts are Redrawing the Competitive Landscape
Regulators are moving quickly in Asia Pacific by introducing frameworks for Open Banking, real-time payments, and even AI-specific standards like Singapore’s AI Verify. But the challenge for banks isn’t just keeping up with evolving external mandates. Internally, many are navigating a complicated mix of overlapping policies, built up over years of compliance with local, regional, and global rules. This often slows down innovation and makes it harder to implement AI and automation consistently across the organisation.
As banks double down on AI, it is clear that governance can’t be an afterthought. Many are still dealing with fragmented ownership of AI systems, inconsistent oversight, and unclear rules around things like model fairness and explainability. The more progressive ones are starting to fix this by setting up centralised governance frameworks, investing in risk-based controls, and putting processes in place to monitor things like bias and model drift from day one. They are not just trying to stay compliant; they are preparing for what’s coming next. In this landscape, the ability to manage regulatory complexity with speed and clarity, both internally and externally, is quickly becoming a competitive edge.
3. Success Depends on Strategy, Not Just Tech
While enthusiasm for AI is high, sustainable success hinges on a clear, aligned strategy that connects technology to business outcomes. Many banks struggle with fragmented initiatives because they lack a unified roadmap that prioritises high-impact use cases. Without clear goals, AI projects often fail to deliver meaningful value, becoming isolated pilots with limited scalability.
To avoid this, banks need to develop robust return-on-investment (ROI) models tailored to their context — measuring benefits like faster credit decisioning, reduced fraud losses, or increased cross-selling effectiveness. These models must consider not only the upfront costs of infrastructure and talent, but also ongoing expenses such as model retraining, governance, and integration with existing systems.
Ethical AI governance is another essential pillar. With growing regulatory scrutiny and public concern about opaque “black box” models, banks must embed transparency, fairness, and accountability into their AI frameworks from the outset. This goes beyond compliance; strong governance builds trust and is key to responsible, long-term use of AI in sensitive, high-stakes financial environments.
4. Legacy Challenges Still Hold Banks Back
Despite strong momentum, many banks face foundational barriers that hinder effective AI deployment. Chief among these is data fragmentation. Core customer, transaction, compliance, and risk data are often scattered across legacy systems and third-party platforms, making it difficult to access the integrated, high-quality data that AI models require.
This limits the development of comprehensive solutions and makes AI implementations slower, costlier, and less effective. Instead of waiting for full system replacements, banks need to invest in integration layers and modern data platforms that unify data sources and make them AI-ready. These platforms can connect siloed systems – such as CRM, payments, and core banking – to deliver a consolidated view, which is crucial for accurate credit scoring, personalised offers, and effective risk management.
Banks must also address talent gaps. The shortage of in-house AI expertise means many institutions rely on external consultants, which increases costs and reduces knowledge transfer. Without building internal capabilities and adjusting existing processes to accommodate AI, even sophisticated models may end up underused or misapplied.
5. Collaboration and Capability Building are Key Enablers
AI transformation isn’t just a technology project – it’s an organisation-wide shift that requires new capabilities, ways of working, and strategic partnerships. Success depends on more than just hiring data scientists. Relationship managers, credit officers, compliance teams, and frontline staff all need to be trained to understand and act on AI-driven insights. Processes such as loan approvals, fraud escalations, and customer engagement must be redesigned to integrate AI outputs seamlessly.
To drive continuous innovation, banks should establish internal Centres of Excellence for AI. These hubs can lead experimentation with high-value use cases like predictive credit scoring or real-time fraud detection, while ensuring that learnings are shared across business units. They also help avoid duplication and promote strategic alignment.
Partnerships with fintechs, technology providers, and academic institutions play a vital role as well. These collaborations offer access to cutting-edge tools, niche expertise, and locally relevant AI models that reflect the regulatory, cultural, and linguistic contexts banks operate in. In a fast-moving and increasingly competitive space, this combination of internal capability building and external collaboration gives banks the agility and foresight to lead.
Southeast Asia’s banking sector is poised for significant digital transformation. With projected Net Interest Income reaching USD 148 billion by 2024, the market is ripe for continued growth. While traditional banks still hold a dominant position, digital players are making significant inroads. To thrive in this evolving landscape, financial institutions must adapt to rising customer expectations, stringent regulations, and the imperative for resilience. This will require a seamless collaboration between technology and business teams.
To uncover how banks in Southeast Asia are navigating this complex landscape and what it takes to succeed, Ecosystm engaged in in-depth conversations with senior banking executives and technology leaders as part of our research initiatives. Here are the highlights of the discussions with leaders across the region.
#1 Achieving Hyper-Personalisation Through AI
As banks strive to deliver highly personalised financial services, AI-driven models are becoming increasingly essential. These models analyse customer behaviour to anticipate needs, predict future behaviour, and offer relevant services at the right time. AI-powered tools like chatbots and virtual assistants further enhance real-time customer support.
Hyper-personalisation, while promising, comes with its challenges – particularly around data privacy and security. To deliver deeply tailored services, banks must collect extensive customer information, which raises the question: how can they ensure this sensitive data remains protected?
AI projects require a delicate balance between innovation and regulatory compliance. Regulations often serve as the right set of guardrails within which banks can innovate. However, banks – especially those with cross-border operations – must establish internal guidelines that consider the regulatory landscape of multiple jurisdictions.
#2 Beyond AI: Other Emerging Technologies
AI isn’t the only emerging technology reshaping Southeast Asian banking. Banks are increasingly adopting technologies like Robotic Process Automation (RPA) and blockchain to boost efficiency and engagement. RPA is automating repetitive tasks, such as data entry and compliance checks, freeing up staff for higher-value work. CIMB in Malaysia reports seeing a 35-50% productivity increase thanks to RPA. Blockchain is being explored for secure, transparent transactions, especially cross-border payments. The Asian Development Bank successfully trialled blockchain for faster, safer bond settlements. While AR and VR are still emerging in banking, they offer potential for enhanced customer engagement. Banks are experimenting with immersive experiences like virtual branch visits and interactive financial education tools.
The convergence of these emerging technologies will drive innovation and meet the rising demand for seamless, secure, and personalised banking services in the digital age. This is particularly true for banks that have the foresight to future-proof their tech foundation as part of their ongoing modernisation efforts. Emerging technologies offer exciting opportunities to enhance customer engagement, but they shouldn’t be used merely as marketing gimmicks. The focus must be on delivering tangible benefits that improve customer outcomes.
#3 Greater Banking-Fintech Collaboration
The digital payments landscape in Southeast Asia is experiencing rapid growth, with a projected 10% increase between 2024-2028. Digital wallets and contactless payments are becoming the norm, and platforms like GrabPay, GoPay, and ShopeePay are dominating the market. These platforms not only offer convenience but also enhance financial inclusion by reaching underbanked populations in remote areas.
The rise of digital payments has significantly impacted traditional banks. To remain relevant in this increasingly cashless society, banks are collaborating with fintech companies to integrate digital payment solutions into their services. For instance, Indonesia’s Bank Mandiri collaborated with digital credit services provider Kredivo to provide customers with access to affordable and convenient credit options.
Partnerships between traditional banks and fintechs are essential for staying competitive in the digital age, especially in areas like digital payments, data analytics, and customer experience.
While these collaborations offer opportunities, they also pose challenges. Banks must invest in advanced fraud detection, AI monitoring, and robust authentication to secure digital payments. Once banks adopt a mindset of collaboration with innovators, they can leverage numerous innovations in the cybersecurity space to address these challenges.
#4 Agile Infrastructure for an Agile Business
While the banking industry is considered a pioneer in implementing digital technologies, its approach to cloud has been more cautious. While interest remained high, balancing security and regulatory concerns with cloud agility impacted the pace. Hybrid multi-cloud environments has accelerated banking cloud adoption.
Leveraging public and private clouds optimises IT costs, offering flexibility and scalability for changing business needs. Hybrid cloud allows resource adjustments for peak demand or cost reductions off-peak. Access to cloud-native services accelerates innovation, enabling rapid application development and improved competitiveness. As the industry adopts GenAI, it requires infrastructure capable of handling vast data, massive computing power, advanced security, and rapid scalability – all strengths of hybrid cloud.
Replicating critical applications and data across multiple locations ensures disaster recovery and business continuity. A multi-cloud strategy also helps avoid vendor lock-in, diversifies cloud providers, and reduces exposure to outages.
Hybrid cloud adoption offers benefits but also presents challenges for banks. Managing the environment is complex, needing coordination across platforms and skilled personnel. Ensuring data security and compliance across on-prem and public cloud infrastructure is demanding, requiring robust measures. Network latency and performance issues can arise, making careful design and optimisation crucial. Integrating on-prem systems with public cloud services is time-consuming and needs investment in tools and expertise.
#5 Cyber Measures to Promote Customer & Stakeholder Trust
The banking sector is undergoing rapid AI-driven digital transformation, focusing on areas like digital customer experiences, fraud detection, and risk assessment. However, this shift also increases cybersecurity risks, with the majority of banking technology leaders anticipate inevitable data breaches and outages.
Key challenges include expanding technology use, such as cloud adoption and AI integration, and employee-related vulnerabilities like phishing. Banks in Southeast Asia are investing heavily in modernising infrastructure, software, and cybersecurity.
Banks must update cybersecurity strategies to detect threats early, minimise damage, and prevent lateral movement within networks.
Employee training, clear security policies, and a culture of security consciousness are critical in preventing breaches.
Regulatory compliance remains a significant concern, but banks are encouraged to move beyond compliance checklists and adopt risk-based, intelligence-led strategies. AI will play a key role in automating compliance and enhancing Security Operations Centres (SOCs), allowing for faster threat detection and response. Ultimately, the BFSI sector must prioritise cybersecurity continuously based on risk, rather than solely on regulatory demands.
Breaking Down Barriers: The Role of Collaboration in Banking Transformation
Successful banking transformation hinges on a seamless collaboration between technology and business teams. By aligning strategies, fostering open communication, and encouraging cross-functional cooperation, banks can effectively leverage emerging technologies to drive innovation, enhance customer experience, and improve efficiency.
A prime example of the power of collaboration is the success of AI initiatives in addressing specific business challenges.
This user-centric approach ensures that technology addresses real business needs.
By fostering a culture of collaboration, banks can promote continuous learning, idea sharing, and innovation, ultimately driving successful transformation and long-term growth in the competitive digital landscape.
The tech industry tends to move in waves, driven by the significant, disruptive changes in technology, such as cloud and smartphones. Sometimes, it is driven by external events that bring tech buyers into sync – such as Y2K and the more recent pandemic. Some tech providers, such as SAP and Microsoft, are big enough to create their own industry waves. The two primary factors shaping the current tech landscape are AI and the consequential layoffs triggered by AI advancements.
While many of the AI startups have been around for over five years, this will be the year they emerge as legitimate solutions providers to organisations. Amidst the acceleration of AI-driven layoffs, individuals from these startups will go on to start new companies, creating the next round of startups that will add value to businesses in the future.
Tech Sourcing Strategies Need to Change
The increase in startups implies a change in the way businesses manage and source their tech solutions. Many organisations are trying to reduce tech debt, by typically consolidating the number of providers and tech platforms. However, leveraging the numerous AI capabilities may mean looking beyond current providers towards some of the many AI startups that are emerging in the region and globally.
The ripple effect of these decisions is significant. If organisations opt to enhance the complexity of their technology architecture and increase the number of vendors under management, the business case must be watertight. There will be less of the trial-and-error approach towards AI from 2023, with a heightened emphasis on clear and measurable value.
AI Startups Worth Monitoring
Here is a selection of AI startups that are already starting to make waves across Asia Pacific and the globe.
- ADVANCE.AI provides digital transformation, fraud prevention, and process automation solutions for enterprise clients. The company offers services in security and compliance, digital identity verification, and biometric solutions. They partner with over 1,000 enterprise clients across Southeast Asia and India across sectors, such as Banking, Fintech, Retail, and eCommerce.
- Megvii is a technology company based in China that specialises in AI, particularly deep learning. The company offers full-stack solutions integrating algorithms, software, hardware, and AI-empowered IoT devices. Products include facial recognition software, image recognition, and deep learning technology for applications such as consumer IoT, city IoT, and supply chain IoT.
- I’mCloud is based in South Korea and specialises in AI, big data, and cloud storage solutions. The company has become a significant player in the AI and big data industry in South Korea. They offer high-quality AI-powered chatbots, including for call centres and interactive educational services.
- H2O.ai provides an AI platform, the H2O AI Cloud, to help businesses, government entities, non-profits, and academic institutions create, deploy, monitor, and share data models or AI applications for various use cases. The platform offers automated machine learning capabilities powered by H2O-3, H2O Hydrogen Torch, and Driverless AI, and is designed to help organisations work more efficiently on their AI projects.
- Frame AI provides an AI-powered customer intelligence platform. The software analyses human interactions and uses AI to understand the driving factors of business outcomes within customer service. It aims to assist executives in making real-time decisions about the customer experience by combining data about customer interactions across various platforms, such as helpdesks, contact centres, and CRM transcripts.
- Uizard offers a rapid, AI-powered UI design tool for designing wireframes, mockups, and prototypes in minutes. The company’s mission is to democratise design and empower non-designers to build digital, interactive products. Uizard’s AI features allow users to generate UI designs from text prompts, convert hand-drawn sketches into wireframes, and transform screenshots into editable designs.
- Moveworks provides an AI platform that is designed to automate employee support. The platform helps employees to automate tasks, find information, query data, receive notifications, and create content across multiple business applications.
- Tome develops a storytelling tool designed to reduce the time required for creating slides. The company’s online platform creates or emphasises points with narration or adds interactive embeds with live data or content from anywhere on the web, 3D renderings, and prototypes.
- Jasper is an AI writing tool designed to assist in generating marketing copy, such as blog posts, product descriptions, company bios, ad copy, and social media captions. It offers features such as text and image AI generation, integration with Grammarly and other Chrome extensions, revision history, auto-save, document sharing, multi-user login, and a plagiarism checker.
- Eightfold AI provides an AI-powered Talent Intelligence Platform to help organisations recruit, retain, and grow a diverse global workforce. The platform uses AI to match the right people to the right projects, based on their skills, potential, and learning ability, enabling organisations to make informed talent decisions. They also offer solutions for diversity, equity, and inclusion (DEI), skills intelligence, and governance, among others.
- Arthur provides a centralised platform for model monitoring. The company’s platform is model and platform agnostic, and monitors machine learning models to ensure they deliver accurate, transparent, and fair results. They also offer services for explainability and bias mitigation.
- DNSFilter is a cloud-based, AI-driven content filtering and threat protection service, that can be deployed and configured within minutes, requiring no software installation.
- Spot AI specialises in building a modern AI Camera System to create safer workplaces and smarter operations for every organisation. The company’s AI Camera System combines cloud and edge computing to make video footage actionable, allowing customers to instantly surface and resolve problems. They offer intelligent video recorders, IP cameras, cloud dashboards, and advanced AI alerts to proactively deliver insights without the need to manually review video footage.
- People.ai is an AI-powered revenue intelligence platform that helps customers win more revenue by providing sales, RevOps, marketing, enablement, and customer success teams with valuable insights. The company’s platform is designed to speed up complex enterprise sales cycles by engaging the right people in the right accounts, ultimately helping teams to sell more and faster with the same headcount.
These examples highlight a few startups worth considering, but the landscape is rich with innovative options for organisations to explore. Similar to other emerging tech sectors, the AI startup market will undergo consolidation over time, and incumbent providers will continue to improve and innovate their own AI capabilities. Till then, these startups will continue to influence enterprise technology adoption and challenge established providers in the market.
Trust in the Banking, Financial Services, and Insurance (BFSI) industry is critical – and this amplifies the value of stolen data and fuels the motivation of malicious actors. Ransomware attacks continue to escalate, underscoring the need for fortified backup, encryption, and intrusion prevention systems. Similarly, phishing schemes have become increasingly sophisticated, placing a burden on BFSI cyber teams to educate employees, inform customers, deploy multifactor authentication, and implement fraud detection systems. While BFSI organisations work to fortify their defences, intruders continually find new avenues for profit – cyber protection is a high-stakes game of technological cat and mouse!
Some of these challenges inherent to the industry include the rise of cryptojacking – the unauthorised use of a BFSI company’s extensive computational resources for cryptocurrency mining.
Building Trust Amidst Expanding Threat Landscape
BFSI organisations face increasing complexity in their IT landscapes. Amidst initiatives like robo-advisory, point-of-sale lending, and personalised engagements – often facilitated by cloud-based fintech providers – they encounter new intricacies. As guest access extends to bank branches and IoT devices proliferate in public settings, vulnerabilities can emerge unexpectedly. Threats may arise from diverse origins, including misconfigured ATMs, unattended security cameras, or even asset trackers. Ensuring security and maintaining customer trust requires BFSI organisations to deploy automated and intelligent security systems to respond to emerging new threats.
Ecosystm research finds that nearly 70% of BFSI organisations have the intention of adopting AI and automation for security operations, over the next two years. But the reality is that adoption is still fairly nascent. Their top cyber focus areas remain data security, risk and compliance management, and application security.
Addressing Alert Fatigue and Control Challenges
According to Ecosystm research, 50% of BFSI organisations use more than 50 security tools to secure their infrastructure – and these are only the known tools. Cyber leaders are not only challenged with finding, assessing, and deploying the right tools, they are also challenged with managing them. Management challenges include a lack of centralised control across assets and applications and handling a high volume of security events and false positives.
Software updates and patches within the IT environment are crucial for security operations to identify and address potential vulnerabilities. Management of the IT environment should be paired with greater automation – event correlation, patching, and access management can all be improved through reduced manual processes.
Security operations teams must contend with the thousands of alerts that they receive each day. As a result, security analysts suffer from alert fatigue and struggle to recognise critical issues and novel threats. There is an urgency to deploy solutions that can help to reduce noise. For many organisations, an AI-augmented security team could de-prioritise 90% of alerts and focus on genuine risks.
Taken a step further, tools like AIOps can not only prioritise alerts but also respond to them. Directing issues to the appropriate people, recommending actions that can be taken by operators directly in a collaboration tool, and rules-based workflows performed automatically are already possible. Additionally, by evaluating past failures and successes, AIOps can learn over time which events are likely to become critical and how to respond to them. This brings us closer to the dream of NoOps, where security operations are completely automated.
Threat Intelligence and Visibility for a Proactive Cyber Approach
New forms of ransomware, phishing schemes, and unidentified vulnerabilities in cloud are emerging to exploit the growing attack surface of financial services organisations. Security operations teams in the BFSI sector spend most of their resources dealing with incoming alerts, leaving them with little time to proactively investigate new threats. It is evident that organisations require a partner that has the scale to maintain a data lake of threats identified by a broad range of customers even within the same industry. For greater predictive capabilities, threat intelligence should be based on research carried out on the dark web to improve situational awareness. These insights can help security operations teams to prepare for future attacks. Regular reporting to keep CIOs and CISOs informed of the changing threat landscape can also ease the mind of executives.
To ensure services can be delivered securely, BFSI organisations require additional visibility of traffic on their networks. The ability to not only inspect traffic as it passes through the firewall but to see activity within the network is critical in these increasingly complex environments. Network traffic anomaly detection uses machine learning to recognise typical traffic patterns and generates alerts for abnormal activity, such as privilege escalation or container escape. The growing acceptance of BYOD has also made device visibility more complex. By employing AI and adopting a zero-trust approach, devices can be profiled and granted appropriate access automatically. Network operators gain visibility of unknown devices and can easily enforce policies on a segmented network.
Intelligent Cyber Strategies
Here is what BFSI CISOs should prioritise to build a cyber resilient organisation.
Automation. The volume of incoming threats has grown beyond the capability of human operators to investigate manually. Increase the level of automation in your SOC to minimise the routine burden on the security operations team and allow them to focus on high-risk threats.
Cyberattack simulation exercises. Many security teams are too busy dealing with day-to-day operations to perform simulation exercises. However, they are a vital component of response planning. Organisation-wide exercises – that include security, IT operations, and communications teams – should be conducted regularly.
An AIOps topology map. Identify where you have reliable data sources that could be analysed by AIOps. Then select a domain by assessing the present level of observability and automation, IT skills gap, frequency of threats, and business criticality. As you add additional domains and the system learns, the value you realise from AIOps will grow.
A trusted intelligence partner. Extend your security operations team by working with a partner that can provide threat intelligence unattainable to most individual organisations. Threat intelligence providers can pool insights gathered from a diversity of client engagements and dedicated researchers. By leveraging the experience of a partner, BFSI organisations can better plan for how they will respond to inevitable breaches.
Conclusion
An effective cybersecurity strategy demands a comprehensive approach that incorporates technology, education, and policies while nurturing a culture of security awareness throughout the organisation. CISOs face the daunting task of safeguarding their organisations against relentless cyber intrusion attempts by cybercriminals, who often leverage cutting-edge automated intrusion technologies.
To maintain an advantage over these threats, cybersecurity teams must have access to continuous threat intelligence; automation will be essential in addressing the shortage of security expertise and managing the overwhelming volume and frequency of security events. Collaborating with a specialised partner possessing both scale and experience is often the answer for organisations that want to augment their cybersecurity teams with intelligent, automated agents capable of swiftly
The global economy remains fragile due to multiple factors; and banking organisations will need to weather the storm. While large and well-capitalised banks are expected to fare better, there is a need for the industry to pursue new sources of value beyond traditional boundaries.
Banking industry leaders should be bold, proactive, and envision possibilities beyond current uncertainties. Technology has a key role to play in turning their innovation and resiliency goals into reality.
Read on to find out how the National Australia Bank, the Scottish National Investment Bank, the ANZ Bank, the Swiss National Bank, Mastercard, and the French banking group Crédit Agricole are leading the charge in driving innovation within the banking industry by investing in new technologies and exploring business models to better serve their customers.
Download ‘The Future of Banking’ as a PDF
November has seen uncertainties in the technology market with news of layoffs and hiring freezes from big names in the industry – Meta, Amazon, Salesforce, and Apple to name a few. These have impacted thousands of people globally, leaving tech talent with one common question, ‘What next?’
While the current situation and economic trends may seem grim, it is not all bad news for tech workers. It is true that people strategies in the sector may be impacted, but there are still plenty of opportunities for tech experts in the industry.
Here is what Ecosystm Analysts say about what’s next for technology workers.
Today, we are seeing two quite conflicting signals in the market: Tech vendors are laying off staff; and IT teams in businesses are struggling to hire the people they need.
At Ecosystm, we still expect a healthy growth in tech spend in 2023 and 2024 regardless of economic conditions. Businesses will be increasing their spend on security and data governance to limit their exposure to cyber-attacks; they will spend on automation to help teams grow productivity with current or lower headcount; they will continue their cloud investments to simplify their technology architectures, increase resilience, and to drive business agility. Security, cloud, data management and analytics, automation, and digital developers will all continue to see employment opportunities.
If this is the case, then why are tech vendors laying off headcount?
The slowdown in the American economy is a big reason. Tech providers that are laying of staff are heavily exposed to the American market.
- Salesforce – 68% Americas
- Facebook – 44% North America
- Genesys – around 60% in North America
Much of the messaging that these providers are giving is it is not that business is performing poorly – it is that growth is slowing down from the fast pace that many were witnessing when digital strategies accelerated.
Some of these tech providers might also be using the opportunity to “trim the fat” from their business – using the opportunity to get rid of the 2-3% of staff or teams that are underperforming. Interestingly, many of the people that are being laid off are from in or around the sales organisation. In some cases, tech providers are trimming products or services from their business and associated product, marketing, and technical staff are also being laid off.
While the majority of the impact is being felt in North America, there are certainly some people being laid off in Asia Pacific too. Particularly in companies where the development is done in Asia (India, China, ASEAN, etc.), there will be some impact when products or services are discontinued.
While it is not all bad news for tech talent, there is undoubtedly some nervousness. So this is what you should think about:
Change your immediate priorities. Ecosystm research found that 40% of digital/IT talent were looking to change employers in 2023. Nearly 60% of them were also thinking of changes in terms of where they live and their career.
This may not be the right time to voluntarily change your job. Job profiles and industry requirements should guide your decision – by February 2023, a clearer image of the job market will emerge. Till then, upskill and get those certifications to stay relevant!
Be prepared for contract roles. With a huge pool of highly skilled technologists on the hunt for new opportunities, smaller technology providers and start-ups have a cause to celebrate. They have faced the challenge of getting the right talent largely because of their inability to match the remunerations offered by large tech firms.
These companies may still not be able to match the benefits offered by the large tech firms – but they provide opportunities to expand your portfolio, industry expertise, and experience in emerging technologies. This will see a change in job profiles. It is expected that more contractual roles will open up for the technology industry. You will have more opportunities to explore the option of working on short-term assignments and consulting projects – sometimes on multiple projects and with multiple clients at the same time.
Think about switching sides. The fact remains that digital and technology upgrades continue to be organisational priorities, across all industries. As organisations continue on their digital journeys, they have an immense potential to address their skills gap now with the availability of highly skilled talent. In a recently conducted Ecosystm roundtable, CIOs reported that new graduates have been demanding salaries as high as USD 200,000 per annum! Even banks and consultancies – typically the top paying businesses – have been finding it hard to afford these skills! These industries may well benefit from the layoffs.
If you look at technology job listings, we see no signs of the demand abating!
In this blog, our guest author Shameek Kundu talks about the importance of making AI/ machine learning models reliable and safe. “Getting data and algorithms right has always been important, particularly in regulated industries such as banking, insurance, life sciences and healthcare. But the bar is much higher now: more data, from more sources, in more formats, feeding more algorithms, with higher stakes.”
Building trust in algorithms is essential. Not (just) because regulators want it, but because it is good for customers and business. The good news is that with the right approach and tooling, it is also achievable.
Getting data and algorithms right has always been important, particularly in regulated industries such as banking, insurance, life sciences and healthcare. But the bar is much higher now: more data, from more sources, in more formats, feeding more algorithms, with higher stakes. With the increased use of Artificial Intelligence/ Machine Learning (AI/ML), today’s algorithms are also more powerful and difficult to understand.
A false dichotomy
At this point in the conversation, I get one of two reactions. One is of distrust in AI/ML and a belief that it should have little role to play in regulated industries. Another is of nonchalance; after all, most of us feel comfortable using ‘black-boxes’ (e.g., airplanes, smartphones) in our daily lives without being able to explain how they work. Why hold AI/ML to special standards?
Both make valid points. But the skeptics miss out on the very real opportunity cost of not using AI/ML – whether it is living with historical biases in human decision-making or simply not being able to do things that are too complex for a human to do, at scale. For example, the use of alternative data and AI/ML has helped bring financial services to many who have never had access before.
On the other hand, cheerleaders for unfettered use of AI/ML might be overlooking the fact that a human being (often with a limited understanding of AI/ML) is always accountable for and/ or impacted by the algorithm. And fairly or otherwise, AI/ML models do elicit concerns around their opacity – among regulators, senior managers, customers and the broader society. In many situations, ensuring that the human can understand the basis of algorithmic decisions is a necessity, not a luxury.
A way forward
Reconciling these seemingly conflicting requirements is possible. But it requires serious commitment from business and data/ analytics leaders – not (just) because regulators demand it, but because it is good for their customers and their business, and the only way to start capturing the full value from AI/ML.
1. ‘Heart’, not just ‘Head’
It is relatively easy to get people excited about experimenting with AI/ML. But when it comes to actually trusting the model to make decisions for us, we humans are likely to put up our defences. Convincing a loan approver, insurance under-writer, medical doctor or front-line sales-person to trust an AI/ML model – over their own knowledge or intuition – is as much about the ‘heart’ as the ‘head’. Helping them understand, on their own terms, how the alternative is at least as good as their current way of doing things, is crucial.
2. A Broad Church
Even in industries/ organisations that recognise the importance of governing AI/ML, there is a tendency to define it narrowly. For example, in Financial Services, one might argue that “an ML model is just another model” and expect existing Model Risk teams to deal with any incremental risks from AI/ML.
There are two issues with this approach:
First, AI/ML models tend to require a greater focus on model quality (e.g., with respect to stability, overfitting and unjust bias) than their traditional alternatives. The pace at which such models are expected to be introduced and re-calibrated is also much higher, stretching traditional model risk management approaches.
Second, poorly designed AI/ML models create second order risks. While not unique to AI/ML, these risks become accentuated due to model complexity, greater dependence on (high-volume, often non-traditional) data and ubiquitous adoption. One example is poor customer experience (e.g., badly communicated decisions) and unfair treatment (e.g., unfair denial of service, discrimination, misselling, inappropriate investment recommendations). Another is around the stability, integrity and competitiveness of financial markets (e.g., unintended collusion with other market players). Obligations under data privacy, sovereignty and security requirements could also become more challenging.
The only way to respond holistically is to bring together a broad coalition – of data managers and scientists, technologists, specialists from risk, compliance, operations and cyber-security, and business leaders.
3. Automate, Automate, Automate
A key driver for the adoption and effectiveness of AI/ ML is scalability. The techniques used to manage traditional models are often inadequate in the face of more data-hungry, widely used and rapidly refreshed AI/ML models. Whether it is during the development and testing phase, formal assessment/ validation or ongoing post-production monitoring, it is impossible to govern AI/ML at scale using manual processes alone.
o, somewhat counter-intuitively, we need more automation if we are to build and sustain trust in AI/ML. As humans are accountable for the outcomes of AI/ ML models, we can only be ‘in charge’ if we have the tools to provide us reliable intelligence on them – before and after they go into production. As the recent experience with model performance during COVID-19 suggests, maintaining trust in AI/ML models is an ongoing task.
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I have heard people say “AI is too important to be left to the experts”. Perhaps. But I am yet to come across an AI/ML practitioner who is not keenly aware of the importance of making their models reliable and safe. What I have noticed is that they often lack suitable tools – to support them in analysing and monitoring models, and to enable conversations to build trust with stakeholders. If AI is to be adopted at scale, that must change.
Shameek Kundu is Chief Strategy Officer and Head of Financial Services at TruEra Inc. TruEra helps enterprises analyse, improve and monitor quality of machine
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As the Knowledge Partner for the Singapore Fintech Festival, Ecosystm has a finger on the pulse of the Financial Services industry.
In this Ecosystm Bytes, we focus on how the Banking industry will look in the decade ahead, why it continues to struggle with compliance and customer experience, and where technology is helping.
Ecosystm Predicts: The Top 5 FinTech Trends for 2021
In 2021, one of the prevalent shifts we are witnessing in banking services is the switch towards automation to enhance the services and customer experience. Want to know more? Create your free account on the Ecosystm platform to access The Top 5 FinTech Trends for 2021 and more from the Ecosystm Predicts Series.
Financial services institutions made a fast switch to remote working when the pandemic forced widespread lockdowns across the globe. The adoption of remote working was nascent in the industry, and there was a need for a fast pivot by both the organisations and employees (Figure 1).
However, this has also exposed the industry to technology-related risks. Ecosystm Principal Advisor, Gerald Mackenzie says, “The move to a more virtualised working environment has been a trend taking shape for many years and like so many trends, it needed an impetus to make it a norm; in this case COVID-19.”
“Many, if not most, financial institutions have been shifting to more cloud-based and modularised Banking-as-a-Service (BaaS) models and these trends will only accelerate as we need to manage risks inherent in conducting financial services via remote working environments. For example, critical capabilities such as Advisor to Client communications need to be verifiable and auditable whether they are happening inside or outside of the office and I predict regulators will be pushing financial institutions to ensure these standards become the norm rather than the exception.”
Singapore Addresses Risk in the Financial Industry
To manage and mitigate risks that could emerge from the extensive remote working adoptions by FIs, The Monetary Authority of Singapore (MAS) and The Association of Banks in Singapore (ABS) jointly released a paper titled Risk Management and Operational Resilience in a Remote Working Environment. This is also in line with the previous collaboration between MAS and ABS in May 2020 to establish the Return to Onsite Operations Taskforce (ROOT). ROOT sought to strengthen and implement safe management and operational resilience measures as well as endorsement of industry best practices.
The paper seeks to create awareness amongst financial institutions on key remote working risks in the domains of technology, operations, security, fraud, staff misconduct, legal and regulatory risks. MAS encourages them to take pre-emptive measures to adopt good practices on managing risks.
Mackenzie adds, “Of course, some of the issues are difficult to solve. For example, staff accessing client data from their homes creates inherent vulnerabilities and the ways to ensure staff have suitable ‘in-home’ working environments to effectively manage these risks can be challenging and expensive. There are great opportunities for innovators to adapt solutions to solve these problems in what will undoubtedly be a growing investment area for many Financial Institutions.” The paper also examines various controls on the people and culture leveraging examples drawn from the experiences of ABS member banks to address evolving risks. For instance, FIs can implement security controls on staff infrastructure including their personal devices, verify in-person meetings against original documents, timely response strategies for recovery teams, legal risks and more.
To keep pace with the changing trends in technology deployment, risk management, and cybersecurity, MAS has been regularly working and engaging with experts to introduce guidelines, principles and best practices for financial institutions. In February, MAS issued a consultation paper proposing revisions to enhance the current requirements for enterprise risk management, investment risk management and public disclosure practices for insurers. Similarly, in January, MAS issued risk management best practice and standards to guide financial institutions in managing technology risk and maintain IT and cyber resilience.
Get insights on the technology areas in the Financial services industry that will see continued investments, as organisations get into the recovery phase.
