Why AI is changing vendor landscapes in banking.

Why AI is changing vendor landscapes in banking?

Looking back at the variety of banking programmes and projects I have led or been part of in the past fifteen years, most projects involve several third parties whose successes or failure has been crucial to the project outcome. I’ve observed significant variance in how effectively project teams have worked with third parties, and how different types of engagement affect the outcome.

 

Reflecting on the problem

In a complex ecosystem of organisations, all with their own way of doing things, and often with different goals, it can be difficult to keep everyone on message. A common theme that spans the successes and failures is the lack of transparency in the management information shared on any technology project.   It is very frustrating to listen to different parties describing one thing as blue, while the other grey, and being unable to reach agreement on the status or progress of a specific task, at any given point in time.  Nine times out of ten resulting in angry executives who see red on a status report, drilling down only to find no one has a clear understanding on what the numbers say - or indeed if they are correct. Why has this remained consistently bad? And going forward what can be done to counteract this. Or simply, why hasn’t it already been done?

 

Being Human not AI

Being human, means we are not inherently good or accurate 100% of the time – especially under the pressures of technology delivery. We are prone to making mistakes and tend to put our own bias and agenda into the creation and reporting of information.  This introduces a perspective into the interpretation of data – be it client versus provider, or vice versa.  Both have valid points of view, but hold different agendas, working (sometimes struggling) towards a common goal – both fates intertwined. Today’s world and the developments in AI augmentation for management information creation and work management allows an opportunity for things to change rapidly, with no major disruptions to current delivery models. It’s an undeniable fact – AI is good at what people are bad at.

 

Any data

With the power of AI, you can take any data structure and teach expert systems, narrow AI, to provide consistent and accurate metrics and reporting without the need of human intervention. Envisage a world without excel!

 

 Data Analysis

A person’s time is mostly spent in meetings, looking at slides or analysing data to look for patterns, problems and trends. AI flourishes in predominantly rule based scenarios. Using multiple data points from multiple sources it can build a picture and validate patterns to make sense of data. More complex measures of unstructured learning and deep tech that can be used within the market surveillance, risk or fraud sectors.

 

Data interaction

A big part of successfully using any tool, is the user’s ability to interact with it. With enhanced data visualisation capabilities although not an AI technology it is a critical enabler for AI adoption. AI is now able to represent complex data in everyman’s terminology. Allowing users to understand and gain key insight from highly complex data in a visual manner, removes the blues and greys from the equation.

 

The problem with AI

A key assumption on the use of AI is that it doesn’t understand the context surrounding the data, leaving room for misinterpretation or failing to recognise an anomaly produced by human error. Although valid points, the problem of contextualised data is being overcome through AI classifying and cross referencing multiple data points and sets through identifying patterns and anomalies and learning from this.  Narrow AI can then use this to validate assumptions it makes at the fraction of the time and cost.

 

What does AI mean for complex vendor landscapes?

The use of AI in today’s world, and in future vendor landscapes is a win/win for both vendors and their clients. Simply put – AI rapidly delivers consistency, while improving MI availability and a reduced overhead in the creation, management and analysis of data. Management teams are relieved from the volatility of simple decision and judgment making. Vendors are better able to keep clients aligned with key information while reducing delivery costs.

 

There’s no doubt AI is contentious at its core, but when weighing in the pros and cons of its use in technology delivery – there is no reason it wouldn’t soon become a staple on any project. Transparency, unbiased judgement, efficiency and favorable to your budget. The failure to adopt could soon leave you behind!