Author: Lori Witzel, Thought Leader Alumnus, Spotfire, a business unit of Cloud Software Group

Today, Artificial Intelligence (AI) is big. AI is massive in terms of its ingestion of information through Large Language Models (LLMs) in the realm of generative AI. It is hugely diverse in terms of its scope across predictive, causal, generative and other AI use case types – and it is widespread and all-encompassing due to its applied relevance across every industry vertical.

Whether we like to call this the fourth or even fifth industrial revolution where levels of human-machine interaction and collaboration reach even loftier heights than many of us ever imagined possible in our lifetimes, the change AI is bringing about in industry is… big.

Boosting business value

Now that both business and technology leaders realise they need to evidence some level of AI optimisation and acceleration to drive new business value, these same departmental leaders also understand that it is vital to grasp the vast potential of AI to accelerate automated insights from predictive intelligence and analytics.

We now sit at a strategic inflection point where the opportunity exists to use pragmatic, insightful and (above all) functionally relevant AI-powered analytics to accelerate operational efficiencies across industries. This is a chance to actually change the way companies work; it’s a chance to create new business models and overhaul operational constructs that have been around for decades.

Simply put, we can now think about accelerating positive forces of digital disruption with new tools inside new methodologies, but inside industries that we already recognise. Let’s consider three very important examples in the shape of finance, manufacturing and healthcare.

Finance, money & banking

As we know, the financial sector runs on numbers. This core fact underlies this industry’s applicability for AI-enriched acceleration and automation. Because our AI engines (of whatever modal type) are designed to drink from large volumes of data to train and learn, the shape of the banking industry is inherently well-aligned with the use of AI technologies.

When we consider the user-level changes in finance and banking that we have seen played out in the last decade about the development of mobile banking and money management, we can immediately see where AI will apply. We need automated intelligence if we are going to build applications that can make decisions faster than any human operative could.

Users already expect instant service and assistance from the applications they use on their desktops and mobile devices. We need AI to keep pace with this new cadence. These same users now expect to be able to access instantaneous decision-making when engaging with historically manual processes related to banking transfers, deposits, trades and actions related to the new world of cryptocurrencies.

As stated in the CA Business Journal, “The advent of digital currencies, artificial intelligence and mobile applications has encouraged the proliferation of startups, which challenge traditional financial institutions by offering tailored services for today’s tech-savvy consumers.”

Manufacturing

It might sound like a simplification, but the manufacturing industry has many parts. Indeed, it has many parts figuratively, operationally and literally. With global supply chains having been jolted so markedly in the wake of the pandemic and other disruptive world events, every businessperson and consumer now has a more acute sense of where production lines are operating and where they are stalled or experiencing outages.

As we now build a more connected and collaborative world, applying AI to everything from error detection in production lines to customer delivery will be crucial to the future success of the manufacturing base in every country.

According to TechTarget, “Manufacturing plants, railroads and other heavy equipment users are increasingly turning to AI-based Predictive maintenance (PdM) to anticipate needs. If equipment isn’t maintained in a timely manner, companies risk losing valuable time and money. On the one hand, they waste money and resources if they perform machine maintenance too early. On the other hand, waiting too long can cause the machine extensive wear and tear.”

On the march towards new levels of efficiency, AI will now empower manufacturing organisations to augment their processes and reach a new level of capability in terms of managing supply chains more efficiently. This will encompass everything from monitoring the incoming stream of raw materials and the management of core utilities, all the way through to tracking parts and products across the manufacturing plant floor… and ultimately down to final deliveries to customers.

Healthcare

While many industries are described as having mission-critical functions, it is surely healthcare that is most directly understood to operate systems, equipment and processes that are life-critical. As we now use AI to crunch through massive volumes of data to detect life-threatening illnesses, healthcare professionals also have the opportunity to use automation to analyse and identify impurities on a medical production line more quickly. These are of course actions that could mean the difference between life and death.

Not only will we now use AI for detection and healthcare systems management, but we will also make use of it to accelerate processes that drive drug discovery and clinical trials. Although we stand at yet another inflection point as we humans start to build trust in AI-enriched medical practices, the industry will ultimately show the true worth of AI in this space as we get used to relying on data-driven solutions to streamline and automate administrative tasks, support physicians and look after more accurate and readily available patient records.

Building a data value chain

All these functions start with data and, crucially, it needs to be data that runs through systems that offer appropriate levels of governance. This means enterprises need to use data virtualization and data management tools that enable automated alerts and process changes so that information is properly prepared for data systems consumption.

It’s at this point that organisations in the three verticals noted here and elsewhere can start to say that they have established a data value chain i.e. an approach to information management that sees a business adopt industry-specific, rapid and pragmatic AI across the enterprise.

This is when a company starts to be able to meet and exceed customers’ expectations and demands. By using AI-enriched predictive and precise analytics, enterprises can now get ahead of their competitors, make real-world reductions in live operational costs and reduce their time-to-decision window in all aspects of business.

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.