Business
Why financial institutions need a modern data architecture
Published
1 month agoon
By
admin
By Stuart Tarmy, Global Director of Financial Services Industry Solutions, Aerospike
It’s a generally accepted truism that data is a key driver of business success. In fact, the phrase ‘Data is the new oil’ was first coined in 2006 by Clive Robert Humby, a British mathematician and entrepreneur. However, this phrase is incomplete, as oil by itself is not useful until it is captured, refined and put to purpose, e.g., changed into gas, plastic or chemicals. Similarly, to be fully exploited, data must also be ‘captured, refined and put to purpose’. In today’s competitive environment that requires an enterprise grade data platform that can operate in real-time, at petabyte scale, be reliable, available in a hybrid model (multi-cloud, on-prem) and provide a low total cost of ownership.
Real-time, and how to achieve this, is critically important. A key concept here is that to develop best-in-class, real-time applications such as fraud, customer360, compliance or risk management, you need to balance the competing needs of utilising the most sophisticated algorithms (often based on AI or more complex neural nets) across the largest amount of relevant, non-correlated data available, and process this in real-time (often less than 30 milliseconds) for a pleasing customer experience.
Given that customers now expect a rapid response and a personalised approach from the financial companies they interact with, real-time data has never been so important. A good example of this is how real-time data is being used to detect fraudulent customer transactions and develop models to predict credit risk.
Processing large amounts of data in real time and delivering insightful analysis cannot be done without a modern data architecture. As part of digital transformation processes, investment needs to be made in the appropriate technologies and systems.
Graph analytics for dealing with fraud
Combating fraudulent activity is a constant, and growing, challenge for financial institutions and one that is high on boardroom agendas if they are to reduce the risk of financial and reputational damage. Some organisations have opted to adopt graph databases, each one of which consists of data elements and the connections between them. The data elements represent a customer or an account, and the connections are the relationships between these entities, which could be social connections, identity or transactions. A graph database works with a real-time data platform, which allows the company to analyse the relationships between the data elements and identify unusual, or suspicious patterns, such as multiple accounts being opened under different names, but with the same IP address.
PayPal is a great example of how to use graph analytics to prevent fraud. It has a bespoke solution which is capable of analysing millions of records within just 20 milliseconds. This can identify fraud risk, allowing the company to put in place prevention processes, thus saving itself and its customers millions in fraud losses.
Document data stores and credit risk management
For the kind of unstructured data that occurs in credit risk management, document data stores are gaining popularity. These document databases collect data from credit bureaus, financial institutions and social media to name a few, and can then provide a detailed overview about whether a borrower is credit worthy. The data can be analysed in real time using machine learning algorithms to identify patterns, trends, and potential risks, so action can be taken to mitigate against them. Risk models are created which will assess a potential creditor’s ability to pay based on their credit history, income and current employment status. If a customer is experiencing financial hardship, a financial services company can act before they default on a payment. Predictive analytics can also be used to develop models that identify potential credit risks before they materialise, which allows credit limits to be adjusted or alternative payment plans to be put in place.
Document data store for powerful personalisation
Any customer-facing operation understands the importance of personalisation when it comes to building strong customer relationships. Financial services companies are striving to enhance personalisation by aggregating data from various sources in real time, including mobile and location-based services.
A document data store is optimised to manage this data in real time and analyse it to build an accurate picture of their customers’ financial behaviour. Using AI and machine learning they can offer tailored product recommendations, personalised financial advice, and targeted marketing campaigns.
Every day the financial services industry generates massive volumes of data. A modern real-time data architecture is essential to help them build best-in-class customer solutions. By analysing customer habits and preferences, personalised product recommendations can be made that better suit their needs and preferences. Personalisation can also lead to customised pricing, credit scoring, interest rates, and loyalty programs, speed up customer onboarding, and predict and prevent customer churn. By using these techniques, financial institutions can beat their competition, enhance the customer experience, improve revenue and grow market share. The alternative is to become less competitive and less relevant to your customers.
Business
How can law firms embrace automation and revolutionise their payments?
Published
18 hours agoon
September 28, 2023By
editorial
Attributed to: Ed Boal, Head of Legal at Shieldpay
Once again, AI is dominating international headlines. This time, it’s due to a closed-door meeting this month between tech leaders and US senators to discuss the technology’s regulation.
AI and automation isn’t just for the likes of Big Tech. We’re seeing predictive and automated technologies transform almost every sector and the legal industry is no exception. In fact, recent research from HBR Consulting found that 60% of law departments had implemented a legal data analytics tool last year and more than 1 in 4 indicated they were using AI for at least a single use case.
However, adoption isn’t without its challenges. Reticence remains among some and there’s also the danger of ‘transformation fatigue’ slowing real progress. If law firms want to reap the many benefits of automation – including revolutionising their payment processes – these challenges need to be carefully considered and thoughtfully addressed.
An area of great opportunity
Often seen as conservative, the legal industry has been gradually warming up to the idea of automation and technology.
While some pioneering firms have been quick to embrace automation tools, others remain cautious about disrupting their established workflows. As we navigate this landscape, it’s clear that certain areas of legal services are ripe for innovation.
One area is contract management. The process of drafting, reviewing, and managing contracts has traditionally been time-consuming and prone to human errors. Automation can alleviate these pain points by streamlining the entire lifecycle of contracts, from creation to renewal, thereby enhancing efficiency and reducing risks.
Another promising domain is legal research. Thanks to advancements in natural language processing and machine learning, legal professionals can now leverage AI-powered research tools that analyse vast volumes of legal data to provide accurate insights and case precedents swiftly.
But, while progress is undoubtedly being made, the legal sector still lags other sectors when it comes to innovation.
What’s getting in the way of progress?
This isn’t always down to a resistance to change. Often, it’s a result of firms spreading their resources too thinly across numerous technology initiatives.

Ed Boal
Attempting to tackle everything at once can result in ‘transformation fatigue’, where the benefits of individual innovations get diluted – leading to frustration and slower progress.
Before legal firms embark on digital transformation projects, a critical first step is introspection. Recognising and acknowledging areas where legacy processes and manual tasks still hold sway is paramount to optimising the impact of automation.
For many firms, archaic practices continue to consume valuable time and resources, diverting attention from higher value, billable tasks. One often-overlooked area is payments.
Legal firms play a critical role in complex transactions, from M&A and real estate deals to litigation and arbitration payments. The associated admin and processes represent a drain of firms’ time and resources. Spanning everything from collating stakeholder payment details and verifying payee identity to ensuring compliance with Know Your Customer (KYC) and Anti Money Laundering (AML) regulation, this adds unnecessary stress for lawyers – who would rather dedicate their time and expertise to their clients’ legal needs.
The repercussions of such time-consuming financial processes reverberate throughout the entire organisation. Administrative burden weighs heavily on the team, affecting productivity and ultimately, the bottom line: recent research from Shieldpay, surveying the UK’s Top 100 law firms, found that almost 1 in 3 (32%) say KYC collection and verification checks take 4-9 working days.
At the same time, firms are exposed to significant financial risk which can make handling client funds a costly endeavour. Not only are they penalised with fines if found to be in breach of stringent client account rules but firms are also subject to hefty premiums for Professional Indemnity (PI) insurance. No wonder 73% of all legal professionals and 90% of junior law professionals are concerned about the risks and time costs associated with holding client funds.
Revolutionising payment transactions
In short, manual payment processes are more than just an inconvenience for modern law firms. They can damage relationships with clients – who have come to expect a fast, painless and automated payout experience in a digital world – and impede revenue generation by tying up top talent in an endless cycle of paperwork and (unbillable) admin.
So how can firms take the pain out of legal payments?
Fortunately, new payment technologies have emerged as a formidable ally. Third-party payment providers offering solutions for law firms, such as escrow and paying agent services for specific transactional deals, or more embedded payment solutions such as managed accounts (TPMAs) – i.e. outsourced client account functions – offer secure and instant transactions, while prioritising transparency and automation.
TPMAs operate as an escrow payment service in which the third-party – a licensed external payments partner – receives and disburses funds on behalf of a firm and their client(s).
With advanced encryption ensuring data security, working with a regulated payment partner means legal professionals and their clients can engage in financial transactions with peace of mind – while law firms benefit from improved operational efficiency.
And the advantages don’t stop there. Enhanced transparency builds a sense of confidence and trust, while the elimination of manual data entry and repetitive tasks allows legal professionals to devote more time to legal services and fostering stronger relationships with their clients.
AI and automation has much to offer the legal sector. But its adoption must be carefully planned in order to avoid transformation fatigue that risks stalling progress altogether. With typically shallower pockets than Big Tech giants, it’s important for law firms to focus their efforts on specific areas that could benefit from automation, rather than rush to overhaul their entire way of working, all at once. This controlled phase-out is the key to avoiding adoption frustration, seeing a real impact on profits and productivity and setting firms up for real, lasting change.
Business
In-platform solutions are only a short-term enhancement, but bespoke AI is the future
Published
2 days agoon
September 27, 2023By
editorial
By Damien Bennett, Global Director, Principal Consultant, Incubeta
If you haven’t heard anyone talking about artificial intelligence (AI) yet, then where have you been? Conversations about AI and its advantages to society have been a key talking point over recent months, with advances being made in the generative AI race and ChatGPT opening a whole plethora of possibilities. Many have highlighted the advantages of AI, but notably it’s ability to create human-like content.
But these discussions have only scratched the surface of what AI is capable of doing. It is for far more than just essay writing, adding Eminem to your rave and photoshopping dogs into pictures.
In marketing, we have been using AI for years, for everything from analyzing customer behaviors to predicting market changes. It’s enabled us to segment customers, forecast sales and provide personalized recommendations, having a huge impact on how our industry works.
It is even, for the more savvy marketers of the world, becoming a key tool in maximizing budget efficiency – which is apt, considering over 70% of CMOs believe they lack sufficient budget to fully execute their 2023 strategy.
Now, as AI becomes more intelligent, the number of efficiencies it can unlock continues to rise. Not only can it help brands get the most out of their available resources and identify any areas of waste, but it can also help highlight new opportunities for growth and maximize the impact of your budget allocation.
The trick, however, is to veer away from the norm of using in-platform solutions with a one-size-fits-all approach and create your own, bespoke solutions that are tailored to your business needs.
Pitfalls of in-platform solutions
In-platform solutions aren’t by any means a bad thing. In fact, built-in AI tools have become increasingly popular, owing to their ease of integration, user-friendly interfaces and minimal set up requirements. They come pre-packaged with the platform, offering the user the ability to leverage AI technologies without the need for in-depth technical expertise or the upfront cost of building a solution from scratch.
However, the streamlined and accessible nature of in-platform AI solutions comes at the expense of complexity and customization. They are designed to serve a broad user base, but for the most part are built using narrow AI solutions with predefined features and workflows.
This makes them great for assisting with common AI tasks, but they lack the flexibility to tailor functionality towards unique business requirements or innovative use cases, limiting the potential efficiencies and cost savings that can be unlocked. Additionally, if a business’ competitors are using the same platform, they are probably using the same AI solution, meaning any strategic advantage gained from these will be reduced.
Bespoke AI solutions, on the other hand, may carry a higher initial investment – but can offer a significantly more attractive ROI over a short amount of time.
Why customized and adapted AI is the key
The difference between bespoke AI and in-platform solutions is similar to that between home cooked food and a microwave meal. Yes, it is more time consuming to prepare, and yes it likely carries more of an upfront cost, but the end result is going to be far more appealing and will carry more long-term value (financially… not nutritionally).
That’s because bespoke solutions, by nature, will have been tailored to address your brands specific needs and challenges. These custom-built tools allow for much greater efficiencies by streamlining workflows across different channels, automating more complex tasks, and providing deeper, more relevant insights.
The increased level of optimization can significantly improve productivity and reduce operational costs over time, offering a higher ROI. The increased flexibility of bespoke AI also allows brands to implement innovative use cases that can significantly differentiate them from their competitors.
The data analyzed can be specifically chosen to match business requirements, as can the outputs of the AI tool, providing a significant advantage when understanding and acting on the insights provided.
Additionally, these tools are, by nature, more scalable. They can be updated, upgraded and expanded as needs change, ensuring they continue delivering value as the business grows. They can also be designed to integrate with any existing IT infrastructure, from CRM systems and databases to marketing platforms and sales tools – leading to more efficient and effective decision-making.
Managing finances with AI
It’s no secret that AI in marketing automation has, and will continue to, revolutionize the way marketing is done. It has a bright, if slightly terrifying, future and can help CMOs to unlock new efficiencies, maximize the impact of their budgets and increase their ROI. And as this technology becomes more advanced, its impact will only increase.
But we already know that…and so does everyone else.
So, in order for businesses to make themselves stand out from the crowd , they must look to fully adopt the power of AI. Creating a customized and unique AI solution could be the way to set yourself apart from your competitors. A bespoke AI tool can provide brands and businesses with features unique to them and their business needs. As a result, companies will benefit from more useful data and better results to make more data-driven decisions for their business. Ultimately, this will help brands to maintain a competitive edge over their competitors, deliver ROI and most importantly optimize their budgets.
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