Tiago Veiga CEO Aurum Solutions predicts 2024


Tiago Veiga, CEO of Aurum Solutions, explains his predictions for the banking and payments sector in 2024.

AI specific

Tech – AI is embedded into customer applications

The rapid development of AI has made it very user-friendly and easy to operate yourself in a relatively short period of time. I see the development as AI platforms being embedded into customer applications – from banking to insurance – be it B2B or B2C. AI is becoming so smart and user-friendly that it automatically handles many elements for the end user and the application owner. This reduces the need for manual entries, filling out forms, searching through data, etc., which helps companies expand and improve their operating margins and also reduces the time required by the customer.

Business – supporting finance teams

We are entering a new era of hyper-personalization, where in the not-too-distant future, finance team members will have an AI tool to automate elements of their specific everyday lives. It knows employees’ workflows, processes, workload, etc. and can help complete those tasks, freeing up time and resources to focus on the more interesting or strategic elements of a role.

For example, AI will act as the first security measure for payment tools, meaning financial professionals will have to deal with the consequences of fraud less often. Another example would be machine learning, which eliminates the need for very time-consuming, complex one-to-many reconciliations and learns over time how thousands of individual payments in one set of accounts match a bulk payment in another account.

AI’s sudden push into the mainstream is actually a redistribution of time wealth – something once reserved only for the most tech-savvy people is now available to those who complete time-consuming tasks without having to upgrade IT infrastructure or learn to code .

AI strategy – Companies need an AI strategy

Companies should use AI. Those that don’t will be left behind because they won’t be able to achieve the scalability and cost-effectiveness that AI tools offer. However, when using AI, caution must be exercised in terms of business strategy and messaging. There are still concerns and nervousness surrounding the cliché of humans being “replaced by robots” that will need to be addressed as AI becomes increasingly part of business operations.

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When defining an AI strategy, companies should evaluate their goals and reasons for incorporating AI. Is this a pure cost-saving measure or is it about doing better business, reducing pressure on teams and creating more personalized user experiences? In my opinion, those who focus solely on cutting costs will lose out in the long run, while those who want to use those costs to drive better business strategy – be it creating new opportunities, entering new markets or increasing market share – will lose big will have advantages. This in turn should create better and more fulfilling jobs for people.

Strategies should consider both the long-term and short-term perspective – what can AI do for a business and/or end user now and in the future? Just looking at one end of the scale is incredibly limiting. For example, simply adding a chatbot to a website and checking the “AI” box doesn’t really realize AI’s potential. A more holistic approach makes more sense and automates processes along the entire customer or employee journey. On the other hand, if a company focuses too much on blue sky thinking about the future rather than the immediate future, it could prevent it from delivering simpler use cases that create value in the here and now.

2024: Data comparison in Fintech

Advanced automation

Data matching is a crucial aspect of the financial industry, especially in the fintech space where data accuracy, speed and consistency are very important to ensure that all regulatory requirements are met.

Automation of data matching processes will continue to evolve, with more advanced APIs, RPA, sophisticated algorithms and machine learning models being used to automate matching tasks. This will result in faster, more accurate and timely voting. This improves the end results as less time is spent on reconciliation – a win-win for financial professionals. However, this is only the case if the automation they use is pure. In other words, end-to-end automation that is data independent, eliminating the need for manual work or intervention.

Real-time voting

Demand for real-time data matching is likely to increase, particularly in industries where instant insights and risk management are critical, such as: B. Finance, iGaming and payments.

In the iGaming industry, for example, real-time polls allow operators to conduct regular spot checks and show them whether their payment gateways are going offline. The importance of this fact cannot be underestimated. There has been an outage by Streamline (now part of WorldPay) in the past and the impact was significant – the direct impact of the outage on revenues totaled between £80m and £300m.

While the ultimate responsibility lies with the gateway in such cases, the ability to perform real-time reconciliation allows operators to identify these issues sooner rather than later, helping to minimize the impact.

Real-time reconciliation not only allows operators to catch technical failures before they cause significant damage, but also keeps pace with the new generation of fraud. You see, real-time interactions are a double-edged sword. Yes, they improve the customer experience, but they also mean that if fraud occurs, the consequences can and will happen quickly if not addressed. Therefore, in our age of real-time digital interactions, it is essential that real-time protection is also in place, such as real-time matching.

Cross-platform integration and third-party data sources

Companies will seek seamless integration between different systems and platforms to ensure smooth data flow and reduce reconciliation discrepancies. The faster the platforms can be integrated, the more precise and up-to-date the end-to-end process becomes.

Flexible voting 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. The ability to integrate data in as many ways as possible ensures that the matching process is as complete and accurate as possible. The more integrated data that can be used, the less manual data manipulation is required, meaning less risk to data integrity.

Organizations should integrate third-party data sources and APIs into their matching processes to access external data for validation and enrichment. Keep in mind that the specific data matching trends for 2024 will depend on factors such as regulatory changes, technological advances, and industry-specific requirements. It’s important to stay up to date on the latest developments in data matching by following industry news and consulting with experts in the field.

Regulatory compliance and auditability

Just as there will always be innovation in the fintech space, there will always be a need for reconciliation. In fact, alignment will become even more important as new developments emerge that increase transaction volumes and add complexity to the already tangled web of fintech tools. To meet the growing need for security, reconciliation solutions place great emphasis on providing detailed audit trails and compliance reporting capabilities.

In reality, however, it is never known what new innovations in the fintech space will come next and what new regulations will in turn be required. Therefore, it is advisable to have a flexible voting solution supported by a team of informed professionals so that compliance pivots can be implemented quickly.

Data analysis and insights

In 2024, reconciliation solutions will become the hub for data analytics. This comes after the last few decades where data was rightly heralded as the new oil; the source that gives companies a competitive advantage. However, during this time some important lessons have been learned about which reconciliation platforms are best suited for implementation. First, data must be handled appropriately to avoid fines and maintain trust with key stakeholders such as customers – matching tools typically offer full audit trials to prove this. Second, data only has the transformative impact that organizations seek when it is accurate – matching tools, by their very nature, ensure organizations have strong data integrity.

Because of these fundamental benefits of matching software, it is no surprise that in 2024 they will move to leveraging the data that passes through them. Reconciliation tools will therefore develop into all-in-one platforms. You will align data, visualize data, and be data independent to capture as much data as possible. They act as a single point of contact for companies to access reports, dashboards and insights and make informed decisions.

Tiago Veiga, CEO of Aurum Solutions


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