How AI is revolutionising data in the banking industry

Zahi Yaari, VP of EMEA, SnapLogic

 

It should go without saying that data in the banking industry is some of the most valuable a business can own. Indeed, for many organisations the data they possess is their most valuable asset and as this data grows in both quality and quantity, so too does its value.

However, many businesses, particularly those in the banking sector, are not getting the most out of their data by allowing silos to form within their organisation, whilst also squandering the talents of their most skilled employees.

When data is locked in different locations, it becomes a burden rather than an asset, weighing down the flow of communication within your business and making simple processes complex and time-consuming.

Take, for example, the most basic line of business processes. Where data silos exist between different teams, the most fundamental back-end tasks become unnecessarily difficult, as chains of communication are weighed down by critical information stored in a variety of different places.

Zahi Yaari

Many banks are essentially building data vaults, inaccessible by other teams in their organisation. Not only can this greatly harm the efficiency of a business’s employees but it also restricts the ability of executives to access valuable data insights.

As the quantity of data increases, banks are rapidly becoming victims of their own success, being hamstrung by the amount of data they possess. What should be their most valuable asset ends up weighing them down and ultimately, it becomes the job of an already overburdened IT staff to step in to help glue together data that should flow seamlessly in the first place.

To make matters worse, IT staff are not the only teams under increasing pressure from unnecessary workloads. Banks are also facing the challenge of legacy technology making even the most basic reporting tasks incredibly labour-intensive.

Month-end processes can weigh down entire finance teams in a mass of administrative tasks, whilst executives wait to gather insights from manual reports.

This is clearly not right, banks in particular need to have the most efficient transfer of information possible, whether it is data in-between teams or insights to the C-suite.

Looking to AI

The realisation that AI-driven automation can both integrate different tools and technologies whilst also freeing up a large proportion of employees’ time has helped the banking industry revolutionise their use of data.

Whilst banking and finance as an industry has often fallen short when it comes to digital transformation, relying on legacy systems for far too long, proactive banking groups are beginning to show that the adoption of cloud technology, integrated systems and process automation can push organisations out ahead of the competition.

Banks that adopt new technologies are changing the competitive landscape within the industry. In particular, cloud-based challenger banks are putting pressure on incumbent banks’ revenues and will continue to do so, unless these banks evolve with the times.

With evolution comes innovation, and AI and machine learning have two key roles here. Firstly, AI-powered integration platforms can glue together different areas of a business, not only allowing for data pipelines to be built with ease, but actually suggesting integrations to the business user with up to 90 percent accuracy. This means that every user can be empowered to take full advantage of advanced technology that can make their work lives far easier and more efficient.

Secondly, once these pipelines are built, AI works to automate highly-repetitive, low-skill tasks that otherwise burden talented teams in manual processes. By learning from billions of existing data flows, these AI-powered platforms can eliminate the data and integration backlog.

The dual benefits of an increase in productivity and efficiency, whilst also cutting down costs and employee workloads makes automation and AI a no-brainer to many executives.

This is particularly true as these executives can benefit from real-time access to data, gaining insights that were impossible with legacy tech and manual processes.

Why now is the time to adopt AI

With the rise in self-service, low-code technology, digital transformation no longer requires a difficult adoption process. In fact, platforms are designed for ease-of-use, meaning companies no longer have to factor in skill shortages or training costs.

One organisation that benefited from this self-service technology was Hampshire Banking Trust, who discovered they could utilise a low-code-no-code infrastructure to quickly deploy these technologies, connecting together their various tools and applications with ease. This meant their slim IT team could easily break down silos within the business whilst also relying on AI to help individual users to manage routine tasks by themselves.

AI integration technology is like the nervous system of a business, passing information from one part to another, whilst also responding to changes and challenges, scaling as needed.

Furthermore, given the vast amounts of highly sensitive information that banks handle and as data regulations continue to evolve, the banking sector needs to stay ahead of the curve; or else poorly-managed data could result in fines and serious reputational damage. Adopting a solution that can not only safeguard critical data and ensure compliance with regulations, but also evolve and grow with the business, is therefore critical to any bank’s future.

This speaks to the larger issue – agility. Where the banking industry has failed to adopt these new technologies, there have been growing challenges caused by inflexible business processes and overburdened staff.

The benefits of adopting AI and ML technologies are clear: they can automate the data infrastructure of a business, both freeing up employees and breaking down silos to create an efficient and flexible business.

Now is the time for banks to reevaluate their data needs and look to AI as the key to unlocking the true value of their data.

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