According to Gartner generative AI, autonomic systems, as well as privacy-enhancing computation, are the 3 technology trends that are gaining traction in banking & investment services in the coming days. These will continue growing over the next 2-3 years and would contribute to the growth as well as the transformation of financial services companies.

Banks as well as investment companies are anticipated to spend over USD 600 billion on technology services. The next phase of Artificial Intelligence - Generative Artificial Intelligence is gaining traction in banking services. Some of the significant tech trends that are impacting Banking include Autonomic Systems & Privacy-Enhancing Computation.

As organizations deal with a humongous amount of data, it becomes really difficult for operationalising and organizing that information for data-driven decision-making. However, at the same time, there are significant areas where you do not possess sufficient data to make sound predictions.

Why banks are purchasing Generative AI

While IT spending by banking & investment firms is predicted for register 6.1 per cent, the largest category of spending is Information Technology services. These include consulting & managed services and account for forty-two percent of total Information Technology spending in the sector at USD 264 billion. The top 3 trends that collectively contribute to goals to not just run, and grow but also transform a business & have demonstrated use cases in the banking industry include:

The application of GANs & NLG

Twenty per cent of all test data for consumer-facing use cases will be synthetically generated by 2025, according to a recent report by Gartner. Generative Artificial Intelligence learns a digital representation of artefacts from data & generates innovative creations which are like the original but however they do not repeat it. In banking & investment services, the application of GANs & natural language generation can be seen in several scenarios such as trading prediction, synthetic data generation, fraud detection, as well as risk factor modelling & it possesses potential owing to its capability for taking personalization to a different level.

Building an agile set of technology capabilities to support new requirements

Autonomic systems are either self-managed physical or software systems which learn from their environments as well as dynamically change their own algorithms in real time for optimizing their behaviour in complex ecosystems. Autonomic systems build an agile set of technology capabilities which buttress new requirements as well as situations, enhance performance as well as defend against attacks without any human intervention.

At present, in the banking context, most of the autonomic systems are software-based. But, humanoid robots are coming up as smart branches and these are the perfect illustrations of hardware-based autonomous systems which cater to clients as well as customers. They can be implemented in several functions such as autonomous debt management, personal finance assistants & automated lending. Although Robo advisors are most importantly low-level autonomic systems, there are still trust concerns owing to their high level of automation.

Protecting personal data processing using Privacy Enhancing Computation

Privacy-enhancing computation ensures personal data processing in unsafe environments and this is highly crucial owing to evolving privacy as well as data protection laws & growing consumer concerns. What privacy-enhancing computation does is it leverages a wide range of privacy-protection techniques for allowing value to be extracted from data while still meeting compliance needs.

Hashstudioz – an artificial intelligence development company works with banks as well as financial institutions on different custom Artificial Intelligence & Machine Language-based models which facilitate improving income, reducing expenses as well as limiting risk in several areas.

Challenges & dangers associated with generative AI

Regardless of all that generative Artificial Intelligence has to provide, it has a set of limitations of the most significant ones include the Generative Adversarial Network models that are unstable & cannot be easy to control sometimes. These at times come up with unexpected outputs, even without any feasible explanation. Although generative Artificial Intelligence can build new content, it may not be creating completely new things but can only be merging the data that it has been fed in new methods. Furthermore, a huge amount of training data is needed for training generative Artificial Intelligence.

Generative Artificial Intelligence can also be implemented by several malicious actors for deceitful purposes such as scamming people, fraudulent activities, fake news & many others. Some generative Artificial Intelligence algorithms are perfect enough for deceiving. And these results, often known as ‘deep fakes’, can be used for masquerading as another person & commit all manners of fraud in their name. For instance, some may try to imitate a person & withdraw money from a bank.

In a nutshell

In the banking sector, the first goal of AI is to assist consumers by making their choices a priority and AI also assists in ensuring that customers are satisfied with the services offered by the bank. Therefore, either the Machine Learning language or the Artificial Intelligence algorithms help banks in understanding consumer expectations.