From artificial intelligence in manufacturing to marketing and other industries, automating processes has proven to help businesses in decision making. Banking is no different; with deep learning and machine learning paving the way for better customer service and lower operational costs, it’s no wonder that banking services are increasing their use of AI tools.

With AI in banking having the potential to offer up to $1 trillion of additional value each year, it becomes paramount to have a strong cloud architecture while still understanding the customer needs, so that services can be delivered at scale and extremely personalized.

In this article, we’ll take a look at AI in banking, namely:

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Best practices for AI in banking

Step 1: Knowing your business needs

By understanding the specific problem you want AI to help solve, you’ll be able to pinpoint what tools will be more beneficial. Sometimes adding a chatbot as part of customer service can be what's needed to lower costs and increase customer satisfaction.

Step 2: Defining how the data will be handled

Banking produces large amounts of data, and knowing how your business will analyze, clean, extract, and centralize it is essential.

Step 3: Giving AI models time to learn

With any AI technology, it’s essential that tools have enough time to learn what’s needed of them so they can deliver the best results. Models need to be fed historical data so they can train themselves, which can be time-consuming if you’re starting from scratch.

Step 4: Automation of testing

AI models need to perform continuous testing, to make sure that results continue to be accurate. Errors in data analysis can lead to bigger issues in your business, either short or long-term, so it’s imperative that you have a system in place to always have your models learning.

AI applications in banking

Predictive analytics

With AI being able to detect patterns and make correlations in data, its general-purpose natural language and semantic applications mean it’s ideal for predictive analysis. Patterns AI can detect are often cross-selling opportunities, potential sales opportunities, and operational data metrics.

Learn more about how machine learning can help you qualify and prioritize leads through predictive analysis.

Improving your marketing analytics through machine learning
Machine learning is a branch of AI that can read all your data and identify ways to improve your marketing efforts. It can enhance your marketing analytics to improve your overall customer experience and help you truly connect with your audience.


Chatbots are extremely common across all industries, as they can deliver a high return on investment (ROI) by saving on costs. They can help with frequently asked questions like fund transfers or balance inquiries, which frees up human employees and reduces workloads in other channels.

Credit scoring and direct lending

Deciding whether clients are creditworthy through the analysis of data from both traditional and non-traditional sources is something AI is helping alternate lenders with. This leads to the creation of innovative systems based on a strong credit scoring model, aiding individuals with a limited credit history.


By leveraging data from previous security threats and learning indicators of potential attacks, AI can help prevent external threats in banking and finance. Additionally, it can also monitor potential internal security risks and offer suggestions on how to correct them, to prevent data theft.

Customer onboarding

Onboarding customers can be challenging, as it’s still document-heavy, and banking services still require undertaking a series of identity checks. AI tools can streamline the onboarding process through document-uploading-automation, facial recognition, or using Optical Character Recognition (OCR) in order to pre-populate data in applications.

Benefits of AI in banking

1. Regulatory compliance

Compliance rules can change regularly as the industry develops, and banking services need to be able to reassure customers that they’re being supported under existing regulations. Customer data protection is something all banking services require, and AI tools can significantly help detect suspicious activity, whether that’s hackers or phishing.

2. Minimized operational costs

Automation in workflows, with the use of natural language processing and machine learning, for example, helps in the operation of repetitive tasks that human employees would potentially not perform as accurately as a machine. Ensuring minimal errors helps to lower operational costs.

Having 24/7 customer support through chatbots has increased banking services’ reliability and credibility, as customers can have quick access to basic information they would otherwise have to spend time on the phone for or go in-branch.

3. Improved customer service

Alongside chatbots improving the customer experience, AI apps have also helped banking services by adding value and increasing customer retention. These apps allow customers access to their bank accounts on public holidays and weekends, where otherwise banks, for example, would ordinarily close.

What value can be added throughout the banking chain?

Front line

  • Marketing
    1. Customer analytics and targeting
    2. Customer engagement
    3. Customer profiling
  • Products and services
    1. Lending
    2. Trading
    3. Product advisory
    4. Investment portals
    5. Financial advice
    6. Payment initiations
    7. Account management
  • Sales and relationships
    1. Automated reconciliation
    2. Know Your Customer (KYC) solutions
    3. Risk management and credit scoring

Mid office

  • Operations
    1. Analytics and research
    2. Compliance
    3. Process automation solutions
    4. Authentication and identity verification

Back office

  • Performance management
    1. Cybersecurity
    2. Predictive recruitment
  • Risk servicing
    1. Early warning systems
    2. Fraud monitoring and detection

Future of AI in banking

Juniper Research states that, by 2024, there will be over 3.6 billion users of digital banking services - a 54% increase from 2020. Digital-only banks and the current digital transformation that’s being seen across the sector are behind this expected increase.

But how are digital-only banks growing so quickly?

According to the study, these digital-only services have tightly focused USPs and offer a superior user experience compared to traditional services. The latter must increase their digital offering to remain competitive, which is often represented by providing an excellent balance between human engagement and digital tools.

Combining customer knowledge with AI tools will lead to more personalized experiences, regardless of customers’ chosen channels. Having a clear data strategy, reimagining operating models, investing in a strong and modern tech core, and building AI use cases are essential for banking services to future-proof themselves.

In addition to using data to offer personalized and intelligent engagements, banking services will also need to improve their back-office operations, innovation processes, and decision-making.

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