According to Forbes, AI has created a transformational change in banking:
“There are so many different applications for AI and they are increasing daily. Essentially, AI has three major technologies: cognitive computing, machine learning and natural language processing. As a result of these digital tools, there has been a significant shift in the banking industry. Bankers are using AI to improve their relationships with customers.”
The Finance industry Banking and Financial Services have gained significant traction over the past decade, bringing in significant investments and transforming how people manage their finances.
Artificial Intelligence (AI) brings the advantage of digitization to banks and helps them meet the competition posed by FinTech players, according to Businesswire. The fintech market reached $131.95 billion in 2022 which is expected to grow to $324 billion by 2026. For traditional banks, this highlights the importance of digital transformation.
Auditing firm Deloitte asserts:
“Digital transformation is the essential bridge between the business of today and the business of tomorrow. For every organization, a strategic approach to digital transformation is crucial. Digital possibilities must shape strategy.”
It’s no surprise that AI has had a tremendous impact on the traditional banking environment. According to joint research conducted by the National Business Research Institute and Narrative Science in 2020, about 32% of banks are already using AI technologies such as predictive analytics, voice recognition, and various others, to have a competitive advantage in the market. In addition, Businesswire notes that the global AI in banking market size was valued at $3.88 billion in 2020 and is projected to reach $64.03 billion by 2030, growing at a CAGR of 32.6% from 2021 to 2030.
However, as banks continue to leverage AI, there are challenges to address, such as ensuring data privacy, maintaining ethical AI practices, and navigating regulatory compliance. Striking the right balance between innovation and responsible AI usage is crucial to reap the full benefits of AI in the finance landscape. As with any transformational change, an effective structure is needed to manage the shift, and that’s where OKRs (Objectives and Key Results) come in.
Let’s look at a few OKRs Examples for Banking Firms that delve into how AI and digital transformation can shape an effective, future-oriented strategy.
OKRs (Objectives and Key Results) is a popular strategy execution framework used by companies across the globe to drive transformational growth. OKRs are especially effective during uncertain business climates, providing a sharp focus on measuring what really matters.
While so much is written about OKRs everywhere, many companies still don’t seem to get it right. This could be attributed to their lack of understanding of OKRs or could also be the inertia of stepping out of their comfort zones around traditional business practices.
Objectives and Key Results can play a significant role in helping banks drive digital transformation by providing a clear and focused framework for achieving strategic objectives in this rapidly evolving landscape.
Here's how OKRs can contribute to a bank's growth in the context of digital transformation:
In summary, OKRs provide banks with a powerful tool to drive growth through digital transformation. This critical change of pace can be made easier and more effective when banks use the OKRs framework to implement their digital transformation strategy.
The digital transformation of banking, fueled by the adoption of AI and other advanced technologies, presents several challenges that banks need to address to ensure successful and responsible implementation.
However, the implementation of these solutions requires that banks are capable of handling customer concerns. As banks introduce AI-driven solutions, customer acceptance, and trust in these technologies become crucial. Banks must educate their customers about the benefits and safeguards of AI while being transparent about how AI is used in decision-making.
Forbes notes that “The future’s looking bright for AI in banking, however, not everyone is convinced this new technology is without its issues. Many are concerned about the closure of bank branches, consumer fear of adopting digital tools and possible increased risk of data breaches.”
In the examples below, we address challenges such as ensuring data privacy, maintaining ethical AI practices, and navigating regulatory compliance. These examples can help banks navigate the fears around AI in banking and improve their relationships with customers.
Banks handle vast amounts of sensitive customer data. The use of AI in processing and analyzing this data introduces concerns about data privacy and security. Ensuring that customer information is adequately protected from unauthorized access/breaches is of utmost importance. Compliance with data protection regulations, such as GDPR or CCPA, becomes critical, as failure to do so can lead to severe legal consequences.
Objective: Enhance data privacy and security measures to safeguard customer information and build trust.
KR 1: Reduce the average time to detect and respond to data breaches by 50%(utilizing AI-driven threat detection and real-time monitoring systems)
KR 2: Reduce the number of major compliance incidents from 38% to 20% over the next six months.
KR 3: Launch 1 pilot for regulatory reporting and data sharing,
KR 4: Achieve a customer satisfaction rating of 85% or higher in AI transparency surveys.
KR 5: Reducing the time spent on compliance checks by 30% and achieving 95%, by implementing an AI tool for identifying potential violations.
AI algorithms are designed to learn from data and make decisions based on patterns and correlations. However, if the data used to train these algorithms contains biases, the AI systems can perpetuate and amplify those biases, leading to unfair or discriminatory outcomes. Banks must take measures to identify and mitigate biases, promote fairness, and ensure transparency in their AI models to maintain ethical AI practices.
Objective: Increase coverage of training and audits on AI Systems inorder to reduce potential vulternabiiities
KR 1: Conduct weekly audits of AI systems to assess fairness and transparency in lending and other critical decision-making processes
KR 2: 100% of all AI development teams to be trained on ethical AI principles and guidelines
KR 3: Increase audit of customer data storage and processing practices from X to 100%
KR 5: Train 100% of employees on data privacy best practices and conduct regular workshops to raise awareness about the importance of safeguarding customer data.
The banking industry is heavily regulated, and the adoption of AI may introduce new complexities when it comes to compliance. Banks must navigate regulatory guidelines to ensure that their AI systems meet the required standards, especially concerning risk management, customer protection, anti-money laundering (AML), and fraud detection. Staying up-to-date with ever-changing regulations and ensuring that AI practices align with compliance requirements can be challenging.
Objective: Utilize AI and digital transformation to streamline regulatory compliance processes and reduce operational risks.
KR 1: Increase completion of the training program on emerging regulations and compliance requirements from 45% to 100%
KR 2: Achieve 100% compliance with data protection regulations and industry standards by implementing robust encryption and access controls.
KR 3: Implement an AI-powered regulatory compliance monitoring system by September 30th
KR 4: Reduce the time taken to address compliance issues in specific categories from 60 days to 20 days.
KR 5: Launch 2 pilots on innovative compliance solutions
We have worked with companies of all sizes to get OKR implementation right. As banks are typically large, legacy enterprises, it’s fitting to caution against the most relevant challenges in OKR implementation.
Here are the top obstacles that can hinder banks from achieving their OKRs:
At Fitbots, our mission is to help companies drive transformational growth with OKRs, KPIs, and initiative/milestone management, by simplifying how they connect their mission to metrics. Fitbots has worked with over 5,000+ teams, helping them get OKRs right and tracking powerful insights on our OKRs software.
With Fitbots, your teams can achieve 10X more by setting & tracking the right outcome metrics, save an average of 450 hours each quarter on report-making and clumsy powerpoints and increase transparency by 100%. We have consistently rated as a High Performer on G2 and are the proud recipients of multiple G2 badges. Our top-rated offerings include:
Click here to book a call with our OKRs expert on how we can help you get OKRs right, and manage them with powerful insights.
Bani is an OKR enthusiast who anchors content and marketing at Fitbots OKRs. She loves spreading the love of OKRs to enrich workplaces and collaborating to create engaging content for her readers.
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