Written by Eli Fathi, CEO, MindBridge Ai
No industry will escape the influence of the tidal wave of change precipitated by artificial intelligence (AI) and the reality is that it will fundamentally change human processes within the next decade. The financial services industry is no exception, with larger firms feeling the pressure of new entrants tackling the “low-hanging fruit” of AI to quickly gain a competitive advantage, while they struggle to define how AI will fit into existing infrastructure, processes, and industry regulations.
This struggle boils down to a set of barriers to adoption, such as data inertia, the perceived lack of technical skills, and how AI fits into existing industry regulations and compliance. These issues are driving some firms to adopt limited forms of technology, such as robotic process automation (RPA), and claim the benefits of AI when they don’t exist.
We must understand these challenges in order to break them down and foster a better path towards unbiased AI adoption for the industry.
Data, data everywhere
Data is the backbone of many business processes, so it’s no surprise that firms turn to AI to help sift through and understand what the data is saying. Financial services organisations encounter additional challenges associated with the vast amount, complexity, and number of their data sources and if these aren’t addressed during the AI adoption process, the problems will only grow.
Rather than treat data as an intangible by-product of business processes, it must be a first-class citizen, respected as a key enterprise asset, and embraced from the CxO level down throughout the organisation. As more data is created and structured, there is also an increased likelihood for privacy, security, and functional risks. Organisations must move away from thinking that data functions are owned by a single department. In some financial institutions, marketing is the custodian of the data, mining it to offer additional services to the customer base, resulting in a limited organisational view of the potential opportunities.
The keys to smoothing the big data problem is to treat all data sources as the crown jewels of the organisation by exercising good data hygiene and clearly defining the relationships and ownerships throughout the organisation. For successful adoption, firms must ensure that all data sources are accessible, understandable, and secure. In addition, firms must build data literacy at the senior levels so that decision makers have enough information to execute clear and realistic strategies.
Innovation elsewhere
Many firms have adopted technologies such as RPA, intelligent automation, or intelligence process automation (IPA) and claim benefits to clients that are like those provided by AI-based solutions. RPA and AI are not interchangeable solutions but rather complementary. RPA is best suited for automating existing rules-based processes that are repetitive, often time-consuming, and based on well-structured data. AI is far more powerful, using the data to help make decisions and predictions based on reinforced learning, providing insights that go beyond the rules. It is AI that will truly revolutionise the way we think, act, and talk about financial services.
Communication and collaboration
As with any new technology, AI is met with a degree of scepticism. There is a huge amount of investment going into the AI space now, however, the outcome of this investment is not often communicated to a wider audience. In order for AI to be seen as truly collaborative, these technologies need to be understood by everyday people, particularly those who are hesitant to understand it. One of the biggest barriers to adoption of AI is people not having a full understanding of how products work, so innovators need to think outside the algorithm box if it going to be adopted on a wider scale.
In AI we trust
The most disruptive element of AI is the ability to codify human intelligence and apply it on a vast scale, but some companies are still hesitant to adopt this revolutionary technology. For AI to be accepted and adopted on a wider scale, organisations must work to build their trust in technology as a genuinely useful tool for collaboration. Taking the world of audit as a use case, the deployment of AI-based solutions not only increases the efficiency of the task but also gives suggestions as to why a certain transaction has been flagged as unusual. AI cannot be seen as just ‘a black box,’ it must be a collaborative tool that humans can understand and use to make decisions based on the provided insights.
AI will breathe much-needed innovation into traditional industries, such as financial institutions, and will make significant impact if firms adopt the technology and incorporate it into their operations effectively, generating new services and delighting clients in new ways.
