Tiago Veiga, CEO of Aurum Solutions sets out his forecasts for the banking and payments sectors in 2024.
The rapid development of AI has seen it become very user friendly and easy to ‘self-service’ in a relatively short time. The way I see it developing is, AI platforms will become embedded into customer applications – from banking to insurance – be they B2B or B2C. AI is becoming so clever and user friendly it will do a lot of elements for the end user and the application owner automatically. This will reduce the need for manual input, form filling, looking through data, etc., helping businesses to scale up and improve operating margins and reducing the time needed from the customer too.
We’re entering a new era of hyper-personalisation where finance team members, in the not-too-distant future, will have an AI tool at their fingertips which can be used to automate elements of their specific day-to-day. It will know people’s workflow, processes, workload, etc. and be able to help complete these tasks, freeing up time and resources to focus on the more interesting or strategic elements of a role.
For instance, AI will act as a first line of security for payment tools, the result being that that finance professionals will have to deal with the implications of fraud less often. Another example would be machine learning chipping away at highly time consuming, complex one-to-many reconciliations, over time learning how thousands of individual payments on one set of accounts match with a bulk one in another account.
AI’s sudden surge into the mainstream is really a redistribution of time wealth – something which was once limited to only the most tech-savvy people is now there for those with time-consuming tasks to use without needing to update IT infrastructure or learn to code.
Businesses should embrace AI. Those who don’t will be left behind as they won’t be able to match the scalability and cost efficiency that AI tools offer. However, caution needs to be taken around business strategy and messaging when deploying AI. There are still concerns and nervousness around the cliché of people being ‘replaced by robots’ which will need to be navigated as AI increasingly becomes part of business operations.
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When defining an AI strategy, businesses should evaluate objectives and why they are incorporating AI. Is it as a pure cost-saving exercise, or is it to create better business, reduce pressure on teams, and create more personalised user experiences? In my opinion, those who are solely focused on cost cutting will lose in the long term, whereas those who want to use it for better business strategy – be that creating new opportunities, winning in new markets, or increasing market share – will benefit greatly. This in turn should create better and more fulfilling jobs for people.
Strategies should consider both the long and short term – what can AI do for a business and / or end users both now and in the future. Only looking at one end of the scale is incredibly limiting. For example, simply adding a chatbot to a website and considering the AI box ticked doesn’t really fulfil the potential of AI. A more holistic approach makes more sense, automating processes across the end-to-end customer or employee journey. On the other hand, if a business focuses too much on the ‘blue sky thinking’ for the future rather than the immediate, it could prevent them from deploying simpler use cases that create value in the here and now.
2024: Data reconciliation in fintech
Data reconciliation is a crucial aspect of the financial industry, particularly in fintech, where data accuracy, speed and consistency are very important to ensure all regulatory requirements are met.
Automation in data reconciliation processes will continue to evolve, with more advanced APIs, RPA, sophisticated algorithms and machine learning models being employed to automate reconciliation tasks. This will lead to faster, more accurate and more timely reconciliation. End results will therefore be improved with less time being spent on reconciliation – a win-win situation for financial professionals. However, this will only be the case if the automation they deploy is pure. In other words, end-to-end automation which is data-agnostic so that no manual work nor interventions are required.
The demand for real-time data reconciliation will likely increase, especially in industries where immediate insights and risk management are critical, such as finance, iGaming and payments.
For example, in the iGaming industry, real-time reconciliations allow operators to conduct regular spot checks, revealing to them if their payment gateways go offline. The importance of this cannot be understated. In the past there was an instance of Streamline (now part of WorldPay) failing, and the fallout was significant – the direct revenue impact of their outage totalled between £80m and £300m.
Whilst responsibility ultimately lies with the gateway in such instances, now that real-time reconciliation is possible, operators can become aware of these issues sooner rather than later, helping to minimise the consequences.
Real-time reconciliation not only allows operators to catch up with fallouts in tech before they rack up considerable levels of damage, it also keeps up with the new generation of fraud. You see, real-time interactions are a double-edged sword. Yes, they improve customer experiences but they also mean that when fraud arises, the consequences can also happen fast and quickly run ahead if unchecked. As a result, in our era of real-time digital interactions, it is imperative that a real-time safeguard is in place too, such as real-time reconciliation.
Organisations will seek seamless integration between different systems and platforms to ensure that data flows smoothly, reducing reconciliation discrepancies. The faster the platforms can be integrated, the more accurate and up to date the end-to-end process will be.
Flexible reconciliation solutions can integrate data sources in many different ways. These include native APIs, RPA processes as well as SFTP and other more traditional delivery methods. Allowing data to be integrated in as many ways as possible ensures that the reconciliation process is as complete and accurate as it can be. The more integrated data that can be used, the less manual manipulation of data is required which means lower risk to data integrity.
Organisations should incorporate third-party data sources and APIs into their reconciliation processes to access external data for validation and enrichment. Remember that the specific trends in data reconciliation for 2024 will depend on factors such as regulatory changes, technological advancements, and industry-specific needs. It’s essential to stay updated with the latest developments in data reconciliation by monitoring industry news and consulting with experts in the field.
Just like how there will always be innovation in fintech, reconciliation will always be required. In fact, as new developments arise, boosting transaction volumes and increasing complexity into the already convoluted web of fintech tools, reconciliation becomes even more important. To match the growing need for security, reconciliation solutions will therefore place a strong emphasis on providing detailed audit trails and compliance reporting features.
However, in reality, it is never known what new innovation will come next in fintech, and in turn, what new regulations will be required. It’s therefore wise to have a flexible reconciliation solution, supported by a team of informed professionals so that compliance pivots can be executed swiftly.
In 2024 reconciliation solutions will become the hub for data analytics. This comes after the last couple of decades when data has rightly been heralded as the new oil; the source which is giving companies a competitive edge. However, in that time there has been some key lessons which reconciliation platforms are perfect for actioning. Firstly, data must be handled appropriately to avoid fines and retain trust in key stakeholders such as clients – reconciliation tools typically offer complete audit trials to prove this. Secondly, data only has the transformative effect which companies seek as long as it is accurate – reconciliation tools by their very nature ensure that businesses are left with strong data integrity.
Due to these foundational assets of reconciliation software, it is not surprising that in 2024 they will be moving to leverage the data which passes through them. Reconciliation tools will therefore transform into all-in-one platforms. They will reconcile data, they will visualise data, and they will be data-agnostic to encompass as much data as possible, acting as a one-stop shop for businesses to access reports, dashboards and insights for informed decisions.