Why the AI in Banking Story Is Yet to Be Written?

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In a world where AI has inundated every sector, enabling organizations to transform various aspects of day-to-day functioning, the adoption in banking is happening at snail’s pace. 

Although the AI landscape is slowly developing within the banking industry, with many realizing the benefits of intelligent automation, several challenges are coming into focus. Balancing the new wave of innovation with data quality concerns and customer trust is a relentless endeavor, which is exactly why the AI in banking story is yet to be written. 

But can banks stay away from this transformative technology for long? Let’s find out! 

AI brings to light several important considerations 

In the banking sector, AI brings to light several key considerations: from fairness and transparency to compliance issues and operational risk. These AI-related issues require banks to pay more emphasis on aspects across data quality and validation, model interpretability, deployment, governance, and more. 

Here are some key considerations: 

Consideration 1: Banking customers want financial companies to understand their needs, anticipate the products and services that best match those needs, and conduct business with them on their preferred terms. But they also are extremely cautious about how banks are approaching data stewardship. It is not easy to strike the right balance between delivering personalized customer experiences while ensuring data privacy, security, and trust. 

Consideration 2: Neural networks and AI algorithms are extremely complex in structure and are rather difficult to understand and implement for the banking workforce, which is largely focused on efficiently running banking operations. For a sector that has been slow in adopting new-age technologies, apprehensions about what the technology is capable of and how it can be leveraged present a greater degree of risk and demands an amplified level of governance.

Consideration 3: The banking sector has principally been a highly regulated one, with governments and regulatory authorities laying down strict guidelines and imposing hefty fines on those non-compliant. To avoid fines, ensure compliance and minimize the risk of losing customer trust, banks must be able to explain the rationale behind AI models and how and where they plan to use them – not just to internal stakeholders but also to regulators, auditors, and customers. 

Consideration 4: It is a known fact that AI algorithms consume humongous amounts of relevant, consistent, and updated data to function effectively – and with greater accuracy and predictability over time. However, in the banking sector, where companies have an overabundance of archaic data, making sure AI models are fed with healthy data is a global concern. Banks need to have mechanisms in place that ensure data is constantly cleansed and transformed for AI algorithms to function appropriately. 

Consideration 5: Although the use cases for AI across other industries are countless, in the banking sector, there haven’t been many success stories – not because the technology does not offer any opportunities but because banks have been too reluctant to embrace the benefits. With no benchmark available, banks are clueless about how to leap into an AI project, the rationale behind AI implementation, or the implications of the technology. Unless banks understand the problems that can be solved with AI, they will fail to achieve executive buy-in and stakeholder/customer trust. 

Taking the right approach is the only way to achieve success 

Customer needs in the banking sector have always been influenced and governed by multiple factors. As customers become increasingly empowered, their expectations have spiraled. With technology changing the face of every industry, ensuring customer trust has become a critical responsibility for banks. If banks want to move from traditional, front-office manual operations and embrace modern innovations that improve the digital customer’s banking experience, they need to embrace technologies like AI to unlock real insights and predict what will drive the customer of tomorrow. 

Here are some ways in which banks can set foot towards AI transformation in the right manner: 

  • Start small. Begin by identifying areas of business that will benefit the most from AI and move ahead with an enterprise-wide rollout once benefits start accruing. 
  • Build AI capability in-house with the help of technology vendors or partner with experienced and capable companies to implement AI – especially across processes that touch the customer. 
  • Educate and empower staff about the need for AI deployment, the impact it can have on workforce efficiency, and the benefits it can bring to customer experience. 
  • Have the right people design and run the AI project. If data scientists are too hard to find, identify, and empower citizen data scientists and give them the tools they need to disseminate awareness about AI. 
  • Establish and maintain a central data governance model that ensures the right level of data quality and compliance. 
  • Have mechanisms in place that ensures AI technology is constantly fed with a steady supply of trusted, good-quality information. 
  • Build awareness and promote transparency about AI, the opportunities it offers, and the risks it presents to ensure continued trust. 

When it comes to AI in banking, there’s a lot AI can bring to the table. Are you ready for the transformation

 

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