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.

Ecosystm research reveals a stark reality: 75% of technology leaders in Financial Services anticipate data breaches. ​

Given the sector’s regulatory environment, data breaches carry substantial financial implications, emphasising the critical importance of giving precedence to cybersecurity. This is compelling a fresh cyber strategy focused on early threat detection and reduction of attack impact.​

Read on to find out how tech leaders are building a culture of cyber-resilience, re-evaluating their cyber policies, and adopting technologies that keep them one step ahead of their adversaries.​

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Fintechs have carved out a niche both in their customer-centric approach and in crafting solutions for underserved communities without access to traditional financial services. Irrespective of their objectives, there is an immense reliance on innovation for lower-cost, personalised, and more convenient services.​

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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Generative AI has stolen the limelight in 2023 from nearly every other technology – and for good reason. The advances made by Generative AI providers have been incredible, with many human “thinking” processes now in line to be automated.  

But before we had Generative AI, there was the run-of-the-mill “traditional AI”. However, despite the traditional tag, these capabilities have a long way to run within your organisation. In fact, they are often easier to implement, have less risk (and more predictability) and are easier to generate business cases for. Traditional AI systems are often already embedded in many applications, systems, and processes, and can easily be purchased as-a-service from many providers.  

Unlocking the Potential of AI Across Industries 

Many organisations around the world are exploring AI solutions today, and the opportunities for improvement are significant: 

• Manufacturers are designing, developing and testing in digital environments, relying on AI to predict product responses to stress and environments. In the future, Generative AI will be called upon to suggest improvements. 
• Retailers are using AI to monitor customer behaviours and predict next steps. Algorithms are being used to drive the best outcome for the customer and the retailer, based on previous behaviours and trained outcomes. 
• Transport and logistics businesses are using AI to minimise fuel usage and driver expenses while maximising delivery loads. Smart route planning and scheduling is ensuring timely deliveries while reducing costs and saving on vehicle maintenance. 
• Warehouses are enhancing the safety of their environments and efficiently moving goods with AI. Through a combination of video analytics, connected IoT devices, and logistical software, they are maximising the potential of their limited space. 
• Public infrastructure providers (such as shopping centres, public transport providers etc) are using AI to monitor public safety. Video analytics and sensors is helping safety and security teams take public safety beyond traditional human monitoring. 

AI Impacts Multiple Roles 

Even within the organisation, different lines of business expect different outcomes for AI implementations. 

• IT teams are monitoring infrastructure, applications, and transactions – to better understand root-cause analysis and predict upcoming failures – using AI. In fact, AIOps, one of the fastest-growing areas of AI, yields substantial productivity gains for tech teams and boosts reliability for both customers and employees. 
• Finance teams are leveraging AI to understand customer payment patterns and automate the issuance of invoices and reminders, a capability increasingly being integrated into modern finance systems. 
• Sales teams are using AI to discover the best prospects to target and what offers they are most likely to respond to.  
• Contact centres are monitoring calls, automating suggestions, summarising records, and scheduling follow-up actions through conversational AI. This is allowing to get agents up to speed in a shorter period, ensuring greater customer satisfaction and increased brand loyalty. 

Transitioning from Low-Risk to AI-Infused Growth 

These are just a tiny selection of the opportunities for AI. And few of these need testing or business cases – many of these capabilities are available out-of-the-box or out of the cloud. They don’t need deep analysis by risk, legal, or cybersecurity teams. They just need a champion to make the call and switch them on.  

One potential downside of Generative AI is that it is drawing unwarranted attention to well-established, low-risk AI applications. Many of these do not require much time from data scientists – and if they do, the challenge is often finding the data and creating the algorithm. Humans can typically understand the logic and rules that the models create – unlike Generative AI, where the outcome cannot be reverse-engineered. 

The opportunity today is to take advantage of the attention that LLMs and other Generative AI engines are getting to incorporate AI into every conceivable aspect of a business. When organisations understand the opportunities for productivity improvements, speed enhancement, better customer outcomes and improved business performance, the spend on AI capabilities will skyrocket. Ecosystm estimates that for most organisations, AI spend will be less than 5% of their total tech spend in 2024 – but it is likely to grow to over 20% within the next 4-5 years. 

It’s been barely one year since we entered the Generative AI Age. On November 30, 2022, OpenAI launched ChatGPT, with no fanfare or promotion. Since then, Generative AI has become arguably the most talked-about tech topic, both in terms of opportunities it may bring and risks that it may carry.

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