Finance
Staying dry during the recessionary storm of 2023
Published
5 months agoon
By
admin
As we enter 2023, many business leaders may be experiencing feelings of uncertainty and apprehension. Heading into a recession and with costs continuing to rise at unprecedented rates, the next 12 months will undoubtedly be tough. But all is not lost. Finance Derivative spoke to five industry experts to determine what we can expect from 2023 and how to weather the storm ahead.
Doing more with less
We have already seen the initial impacts of the looming recession in 2022, as food, fuel and energy costs began to soar. Hugh Scantlebury, CEO and Founder of Aqilla, recognises that this is likely to continue into 2023: “The serious problem for next year comes from inflationary pressures, causing rises in food, fuel, energy, and resources. For businesses and individuals, the cost of living and operating will go up. Although salaries will rise accordingly, all those things must be accounted for, so we will need to keep a much closer eye on what’s coming in, and what’s going out.”
“As the recession takes hold, I wouldn’t be surprised to see the Government viewing fines for data misuse as a way to raise additional cash,” adds Michael Queenan, CEO and Co-Founder of Nephos Technologies. “Not only could this fill a significant fiscal shortfall without hitting voters, it could also strengthen Government support as it presents itself as being serious about data protection. It’s a win-win for the Government so I think it is inevitable that the ICO will be hot on the tails of companies that fall foul of permitted data use.”
“2023 is going to be all about doing more with much less,” notes Bruce Martin, CEO of Tax Systems. “Not only will all businesses be tightening their belts due to rising costs, but particularly in the tax industry, there is a severe shortage of skilled professionals. The main problem is that everyone is embracing technology and, therefore, requires staff with the knowledge to utilise the implemented tech. With this huge increase in demand, the supply of quality developers is being stripped. Simultaneously, we’re not seeing the huge influx of new tax talent needed to meet such demand. This forms the basis of the ongoing ‘war for talent’.”
Automate the future
A key method that will prove crucial in doing more with less will be automation. Scantlebury from Aqilla explains that “automation, artificial intelligence, and machine learning within finance functions can help accounting teams considerably. They can do the heavy lifting, the time-consuming data entry tasks and the repetitive work that can fill up so much of the working day. They also remove much of the grind and monotony — freeing up the time of skilled professionals to add value to the business. Although the finance sector is currently behind the curve in adopting these technologies, hopefully, 2023 will be the year that businesses push and transform the industry once and for all.”
“The manual, monotonous tasks should be automated to free up time for training and development that will accelerate the value being added to the business,” agrees Tax Systems’ Martin. “People don’t want to spend 8 hours a day inputting data into a spreadsheet and they shouldn’t have to when technology can automate such tasks.
“Tax has been lagging behind in the digital revolution that many other industries have experienced in recent years. We have seen the beginnings of this in 2022 but I hope that 2023 will be the year it truly takes off.”
Starting 2023 as we mean to go on
2022 has been a transformative year for the finance industry, as many organisations found new ways to embrace technology. Financial institutions will continue following this trend in 2023 whilst ironing out the creases and righting the wrongs of their journeys so far.
Andrew Doukanaris, Business Director Fintech Europe at Intellias, acknowledges that the success of Buy-Now-Pay-Later (BNPL) payment options will continue over the next 12 months and beyond: “BNPL schemes have become a practically overnight sensation. And in 2023, they are set to continue their ascent. One recent study, conducted in 2021, found the market is set to reach a value of $3.98 trillion by 2030. That’s a huge increase from only $90.69 billion in 2020. And Gen Z’s use of such services grew six-fold in 2021 so it is likely that it will inform consumer behaviour far into the future.”
Similarly, Eyal Sivan, Head of Open Banking at Axway, recognises that open banking hasn’t been as successful as previously predicted: “Although Europe pioneered open banking with their PSD2 regulations, their efforts have been considered by many to be lacklustre at best and an outright failure at worst. Balkanization of standards, inconsistent implementations, and tepid enthusiasm on the part of incumbent banks have led them into Gartner’s Trough of Disillusionment.” But 2023 could be the year that Europe catches up and reaps the technology’s benefits. “However, as the Europeans observed the successes of those that followed, notably in Brazil and the Middle East, they started to revisit their approaches. While PSD2 was centred around payments with data sharing added afterward, the impending updates to legislation (by the name PSD3 or otherwise) will more than likely have a broader focus on generalised data sharing, open finance, and even open data, as Europe catches up to its peers.”
Equally, “Operational Resilience regulation is the dominant theme on the regulatory agenda”, notes Gary Lynam, Director of ERM Advisory at Protecht. “Resilience is shifting the organisational mindset and very much seen as a catalyst for change. What is needed is a structured approach to building operational resilience maturity over time. There is no room for cutting corners. We are likely to see high-profile cases of hefty fines for those financial institutions that fail to successfully demonstrate their ability to recover from stressed events. It is well advised to get ahead of the game in building accountability and tolerance against potential operational disruption, not only to meet incoming new legislation but to be ready for potential disruptive events that could be on the horizon.”
Yet, it is impossible to truly predict what the next year has in store for us – the last couple of years have certainly been unpredictable! As Aqilla’s Scantlebury concludes, “Ultimately, who knows what will happen next year?! We didn’t know there was going to be a war in Ukraine and we didn’t see the energy crisis coming. So, there are a lot of unknowns as we head into 2023…” All we can do is keep our fingers crossed that they are positive surprises!
Business
Unlocking the Power of Data: Revolutionising Business Success in the Financial Services Sector
Published
15 hours agoon
June 8, 2023By
admin
Suki Dhuphar, Head of EMEA, Tamr
The financial services (FS) sector operates within an immensely data-abundant landscape. But it’s well-known that many organisations in the sector struggle to make data-driven decisions because they lack access to the right data to make decisions at the right time.
As the sector strives for a data-driven approach, companies focus on democratising data, granting non-technical users the ability to work with and leverage data for informed decision-making. However, dirty data, riddled with errors and inconsistencies, can lead to flawed analytics and decision-making. Siloed data across departments like Marketing, Sales, Operations, or R&D exacerbates this issue. Breaking down these barriers is essential for effective data democratisation and achieving accurate insights for decision-making.
An antidote to dirty, disconnected data
Overcoming the challenges presented by dirty, disconnected data is not a new problem. But, there are new solutions – such as shifting strategies to focus on data products – which are proven to deliver great results. But, what is a data product?
Data products are high-quality, accessible datasets that organisations use to solve business challenges. Data products are comprehensive, clean, and continuously updated. They make data tangible to serve specific purposes defined by consumers and provide value because they are easy to find and use. For example, an investment firm can benefit from data products to gain insights into market trends and attract more capital. These offer a scalable solution for connecting alternative data sources, providing accurate and continuously updated views of portfolio companies. Using machine learning (ML) based technology enables the data product to adapt to new data sources, giving a firm’s partners confidence in their investment decisions.

Suki Dhuphar
But, before companies can reap the benefits of data products, the development of a robust data product strategy is a must.
Where to begin?
Prior to embarking on a data product strategy, it is imperative to establish clear-cut objectives that align with your organisation’s overarching business goals. Taking an incremental approach enables you to make a real impact against a specific objective – such as streamlining operations to enhance cost efficiency or reshaping business portfolios to drive growth – by starting with a more manageable goal and then building upon it as the use case is proved. For companies that find themselves uncertain about where to begin their move to data products, tackling your customer data is a good place to start for some quick wins to increase the success of the customer experience programmes.
Getting a good grasp on data
Once an objective is in place, it’s time for an organisation to assess its capabilities for executing the data product strategy. To do this, you need to dig into the nitty-gritty details like where the data is, how accurate and complete it is, how often it gets updated, and how well it’s integrated across different departments. This will give a solid grasp of the actual quality of the data and help allocate resources more efficiently. At this stage, you should also think about which stakeholders from across the business from leadership to IT will need to be involved in the process and how.
Once that’s covered, you can start putting together a skilled team and assigning responsibilities to kick-off the creation and management of a comprehensive data platform that spans all relevant departments. This process also helps spot any gaps early on, so you can focus on targeted initiatives.
Identifying the problem you will solve
Now let’s move on to the next step in our data product strategy. Here we need to identify a specific problem or challenge that is commonly faced in your organisation. It’s likely that leaders in different departments, like R&D or procurement, encounter obstacles that hinder their objectives that could be overcome with better insight and information. By defining a clear use case, you will build a real solution to a challenge they are facing rather than a data product for the sake of having data. This will be an impactful case study for your entire organisation to understand the potential benefits of data products and increase appetite for future projects.
Getting buy-in from the business
Once you have identified the problem you want to solve, you need to secure the funding, support, and resources to move the project ahead. To do that, you must present a practical roadmap that shows how you will quickly deliver value. You should also showcase how to improve it over time once the initial use case is proven.
The plan should map how you will measure success effectively with specific indicators (such as KPIs) that are closely tied to business goals. These indicators will give you a benchmark of what success looks like so you can clearly show when you’ve delivered it.
Getting the most out of your data product
Once you’ve got the green light – and the funds – it’s time to put your plan into action by creating a basic version of your data product, also known as a minimum viable data product (MVDP). By starting small and gradually enhancing with each new release you are putting yourself in the best stead to encourage adoption and also (coming back to our iterative approach) help you secure more resources and funding down the line.
To make the most of your data product, it’s essential to tap into the knowledge and experience of business partners as they know how to make the most of the data product and integrate it into existing workflows. Additionally, collecting feedback and using it to improve future releases will bring even more value to end users in the business and, in turn, your customers.
Unlocking the power of data (products)
It’s crucial for companies in FS to make the most of the huge amount of data they have at their disposal. It simply doesn’t make sense to leave this data tapped and not use it to solve real challenges for end users in the business and, in turn, improve the customer experience! By adopting effective strategies for data products, FS organisations can start to maximise the incredible value of their data.
Finance
Preventing fraud and detecting money laundering in real-time
Published
1 day agoon
June 8, 2023By
admin
Mathew Hobbis – Chief Architect FSI, Solace
The number of payment channels has grown exponentially. The time it takes to settle a transaction has gone down from days to minutes. Traditional banks have had to move from a couple of channels to potentially 10-15 within their organisation. The more channels, the more vulnerable the system becomes to fraudsters and criminals. The two big challenges for financial institutions right now are payments fraud at the consumer end of the spectrum, and the growing threat of organisational money laundering.
Here’s the conundrum. Modern financial organisations have to mitigate against such criminal activity for the safety of their users and its own reputation. But they must do this without adding any friction into the payments process that would put off or dissuade users of their services.
They need a solution that can not only keep pace but can carry out the additional checks in real-time across systems that often encompass legacy, on-premises deployments, as well as modern container deployments, and public cloud for AI and ML capabilities. In the real-time world of today, this can only mean using the new generation of event-driven architecture (EDA).
The more channels, the more opportunities for payments fraud
McKinsey charts a rise in fraud in a recent article series: “Skyrocketing levels of fraud, enabled by the accelerated adoption of digital commerce and the ever-increasing sophistication of fraudsters, have overwhelmed traditional controls in recent years. This surge has led to increased fraud losses and damaged customers’ experience and trust.”
For retail banks, payments fraud impacts both consumers and their bottom line. The Association for Financial Professionals®’ latest Payments Fraud and Control Survey, underwritten by J.P. Morgan, found 71% of financial professionals report their organisations were victims of payments fraud. Not only do fraudulent payments negatively impact banking customer experience and confidence, the cumulative cost is also large – one recent study by Juniper Research warns online payment fraud losses alone will globally reach $343 billion between 2023 and 2027.
Anti-money laundering (AML) spells the danger of more serious crimes
Money laundering is a major threat for banks because it usually goes hand in hand with serious organised crimes – including drug or people trafficking, weapons dealing or even terrorism.
The estimated amount of money laundered globally is between 2 and 5% of global GDP – and the reputational damage of undetected money laundering can be catastrophic. The Bank for International Settlements also explains “spotting different money laundering patterns is complex, requiring different data points and data sources as well as the ability to connect them across different systems in order to better identify suspicious flows and patterns.”
There are three key areas where technology and event-driven architecture (EDA) can help address these growing threats. The first is the tech to help you better detect. Banking and payments organisations must be able to quickly identify and action these fraudulent or criminal transactions, across all channels. Many are turning to data modelling and Artificial Intelligence (AI) and Machine Learning (ML) that can learn to recognise questionable transactions. But this can be further enhanced with EDA to manage fraudulent and money laundering transactions at scale.
The second issue and challenge for organisations is speed, specifically feeding transaction data, in real-time, to the AI / ML processes which often live in the public cloud. This is where EDA provides the real-time integration allowing legacy core-banking/mainframe systems to communicate with modern micro-service payment frameworks and cloud-based AI/ML for fraud and anti-money laundering (AML).
Finally, they must be able to stay one step ahead. EDA and the Event Mesh allows flexibility in how software components are wired together and flexibility in where they are located. This allows the platform to ‘evolve’, to react quickly and effectively to changes in the financial landscape. Flexibility, or ‘re-wiring’, and platform evolution needs to be a ‘business as usual’ activity as fraud and fraud detection is a constantly evolving game where financial institutions are pitted against criminals. Who can act the fastest wins.
Building a model – it all starts with scoring transactional data and setting triggers
The sort of activities that go into building a fraud prevention or anti-money laundering model with setting trigger points would include: type of transaction vs. is this consistent with a customer’s previous transaction history? Is it in an expected geography? If they travel a lot, then is the time and travel distance between their last transaction and this transaction reasonable? All this data must be fed into the model and assigned a score.
The score also depends on authentication requests. So typically, if you can identify a user together with their mobile phone, banks may pass the transaction because they are comfortable they know who the user is. But if a similar scenario occurs where the user has reached the same score, but there is no biometric data or mobile authentication, then this would be highly likely to trigger a different reaction – blocking or flagging the questionable transaction for escalation.
Now add AI and ML – fraud and money laundering detection starts to get powerful
When a bank has built a database of models, new transactions can then be checked against the models, and given an accumulated score, AI and machine learning then step up to the plate. These technologies, aided by EDA, can make rapid decisions and enable companies to flag abnormal transactions in real-time across all channels.
Layering these data models with AI/ML offers an opportunity for banks to get out in front and gain ground on fraudsters and money launderers. McKinsey research sees “Recent enhancements in machine learning are helping banks to improve their anti-money-laundering programs significantly, including, and most immediately, the transaction monitoring element of these programs.”
To be fully effective, AI/ML needs a big data set. They can only make decisions based on access to historic datasets. So, the first thing a bank has to do is to ‘train’ the model by buying data or scraping from its own historical datasets. And then the model runs through several fraudulent transactions, so it is now ‘trained’ on what a fraudulent transaction looks like. The objective is to build an understanding so AI/ML can pick out the right (fraudulent) activities.
Event-driven architecture helps police fraud and money laundering faster than ever before
Ideally, banks should build one model set for fraud and one model set for money laundering – then implement both models across all transactions and payment channels. And this is where event-driven architecture (EDA) enables them to leverage their fraud and money laundering data models and use AI/ML technology in real-time across an ever-expanding number of payment channels.
EDA allows banks to build an enterprise IT architecture that lets information flow between applications, microservices, and connected devices in a real-time manner as events occur throughout the business.
Meet the event broker who understands it all
EDA works with a middleman known as an event broker, which enables what’s called loose coupling of applications. This is essential because it means applications and devices don’t need to know where they are sending information, or where the information they’re consuming comes from. But the event broker does.
So, in the event-driven world, a bank just has to make sure a payments channel just sends the right event to communicate with the fraud detection or the anti-money laundering system and receive the same events to get the “yes or no” back.
The alternative is not really an option
It’s a much easier integration than trying to do this via standard REST APIs – which becomes a lot more challenging and will need to be built differently for every different channel a bank has now, plus any new channels. This means banks may have to change models based on not only changes in user behaviour, but changes driven by new products and services or to counter new types of fraud or money laundering.
With standard REST APIs – every time a bank adds a new channel, it has to change the way anti-money laundering and fraud systems work, because they have to know about this other channel. In the event-driven world they don’t know, don’t need to know – and they don’t care!
Banks can accurately support a high volume of transactions in the quickest response time, balance transaction authentication and authorisation with fraud detection without decreasing customer satisfaction, and route events securely across the whole payments ecosystem with efficiency.
A platform for the future – EDA opens the door to manage technical debt and quickly introduce new channels
EDA also provides a platform for the future – allowing banks to innovate outside of just countering fraud and money laundering. EDA will help traditional banks compete in the new world as they need to deliver products and services faster in order to compete. A large bank, with its legacy systems, can now compete against an online mortgage lender—and deliver a broader portfolio of products to customers with more speed.”
Yes, newer fintech market entrants have significantly less technical debt than traditional financial institutions. Imagine a new FX rate provider that can provide payments to every country and give customers the best FX rates. Everything is built on a modern infrastructure anyway – there is no legacy core banking app, and everything is microservice, as everything is in the cloud.
But EDA as an approach to enterprise IT architecture can help traditional banks introduce new services and link applications quickly and at scale, ensuring they can match these agile competitors and provide customers with the instant kind of feedback they seek from their banking services, while not being held back by large volumes of existing technical debt.
EDA – keeping financial institutions one step ahead
The challenge for larger banks is to move more towards real-time – even with a large amount of technical debt. EDA not only provides the springboard to payment modernisation; it also ensures a proliferation of payment channels does not come at the cost of increased fraud and money laundering.
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