Senior Vice President, Digital Transformation, CriticalRiver | Top 100 Diverse Leaders | FinTech | Cloud, AI, ML.
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Do you have a digital wealth management application for your investment portfolio that recommends investing in specific funds? You are likely using artificial intelligence (AI) to manage your money.
From automating and optimizing processes to using conversational AI for enhanced customer engagement and fraud detection, AI and machine learning (ML) are leaving an indelible mark on banks and financial institution performance, completely disrupting the financial industry.
In fact, the global market for AI in banking is expected to reach $64.03 billion by 2030. Today, 80% of banks are very aware of the potential benefits of implementing AI, and a majority are looking to deploy AI-enabled solutions. A lot of these organizations, however, are still struggling to provide basic digital capabilities.
In this article, I will look at how AI/ML is helping overcome industry challenges as well as how the industry can overcome roadblocks preventing the adoption of AI/ML.
How AI/ML Addresses Industry Challenges
AI/ML is enabling financial institutions to address some of their major challenges, including:
1. Cybercrime And Fraud Management
Cybercrime and data breaches are perhaps one of the biggest challenges faced by the financial industry. As of 2022, the average cost of a data breach in the financial sector worldwide stood at $5.97 million. The Financial Stability Board also cautioned in 2020 that "a major cyber incident, if not properly contained, could seriously disrupt financial systems, including critical financial infrastructure, leading to broader financial stability implications."
Applying AI/ML algorithms can flag potential breaches before they happen in real time by monitoring customer behavior. Data analytics-based fraud detection becomes more effective by applying AI to massive customer behavior data. Similarly, it can help block fraudulent transactions and send alerts to the customer. Harnessing conversational AI can also help authenticate customers and prevent unauthorized access to accounts.
2. Customer Experience And Engagement
The pandemic led to a paradigm shift in how people bank and deal with financial organizations. This evolution is likely permanent.
With these shifts, banks and financial institutions are turning to AI/ML to improve services and deliver personalized experiences in the face of serving a highly demanding customer base. From faster loan processing to providing hyper-customized products, AL/ML is reinventing the meaning of customer engagement and loyalty in financial institutions.
3. Adopting A Mobile-First Strategy
With almost everything in their palm, customers today seek out mobile-first, self-service options and prefer to engage with their banks through a mobile app. Banks may not operate 24/7, but customers expect their finances to be available to them when they want.
By integrating AI into mobile apps, financial organizations, particularly banks, can offer personalized prompts and reminders for bills, low balances, suspicious transactions and more. Such measures go a long way in providing an elevated user experience and empowering customers to track their expenses.
4. Automating Routine Processes
Robotic process automation (RPA) has been shown to be able to automate up to 80% of basic work processes, enabling knowledge workers to focus on core operations that require human intervention. This not only speeds up processes, ensuring faster time to loan processing, account opening and more, but can also improve productivity and the employee experience.
The Road Ahead
AI and ML will certainly play an instrumental role in modern, new-age banks and financial firms. Adopting AI makes business sense for banking leaders looking to architect next-gen organizations, and AI pioneers in the industry are already reaping benefits.
That said, there are several roadblocks that financial institutions should be aware of in order to successfully implement AI/ML. Here are a few of the key ones along with some advice about how to overcome these challenges:
1. Agility (Model Drifts Due To Changing Data)
• Ninety-nine percent of the challenges are infrastructure-related (stale/duplicate data) and 1% is implementing machine learning models. Focus on addressing the infrastructure-related challenges first.
• Also, empower citizen data scientists. When considering pre-packaged AI/ML models, ask how they can help in doing so.
2. Data Prep And Cleansing
• You'll need to establish unified data (search and discovery) processes, including preprocessing like cleansing.
• Provide access to no-friction, unaltered, unfiltered raw data for model accuracy.
3. DevOps
• It is key to create production-like data copies in the lower environment.
• For end-to-end self-service model deployment, you'll need proper orchestration.
• Likewise, automation of metadata and lineage should be considered for the entire data governance tooling ecosystem.
4. Cloud
• Be sure to architect on-premises systems for near-future scalability, such as with ML as a Service containerization.
• Also, address cloud-only applicability with evolving enterprise grade end-to-end machine learning workflows.
5. Identify Correct ML Models
You will need to create the proper models for each of the challenges mentioned above. Here are a few of the models that can help with each situation:
• Cybercrime And Fraud Management: Regression methods can forecast and compare network packet parameters to the normal situation. Classification algorithms can detect scanning and spoofing, while regression algorithms can detect anomalous user behaviors such as logging in at an odd time or from an unusual location, network or device. Classification algorithms can also classify individuals for peer-group analysis, cluster users into groups and monitor outliers.
• Customer Experience And Engagement: Natural language processing (NLP), sentiment analysis and recommendation algorithms can customize user experiences. Predictive analytics can help firms identify the root cause of the churn rate and correct it.
• Adopting A Mobile-First Strategy: Clustering algorithms can help establish separate groups of users for targeted marketing, product suggestions, discounts and offers based on user activity data and profiles.
• Automating Routine Processes: Intelligent character recognition (ICR) and optical character recognition (OCR) used in document scanning and processing can help transform data into a structured format, which aids in the faster processing of checks and other documents.
In order to solve these challenges, don’t start from scratch or assume cloud providers will do the magic. You'll need to find a way to build an internal team of data scientists as well as identify partners who can help your organization achieve its specific goals.
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