Machine Learning and Artificial Intelligence in Banking
Every company’s AI journey is different. We’re all trying to figure out how we use AI for our business or our productivity and development. We’re all on different paths and at different parts of the journey. It also may lead to different places.
Current status of AI/ML in business
In just 15 years, 52 percent of the S&P 500 has vanished, according to CB Insights. A lot of this can be attributed to technology. That’s why many companies pay attention to any development in technology these days. Example: when blockchain was big hype in 2017. Many companies did not want to lose out on that. It’s the same thing now with machine learning.
Why do companies disappear from the S&P 500? Incumbents often lack ability to adapt and incorporate emerging technologies faster than startups gain distribution.
Defining AI/ML for business
AI is a part of big data analytics. AI has been pursued through machine learning. Machine learning can mean:
- supervised learning
- reinforcement learning
- unsupervised learning.
In order to have AI in a corporation, you need to have the ecosystem to support it:
- AI and machine learning
- Robotic process automation and orchestration
- Data and processing capacity
- Secure cloud enablement
- Strong customer authentication choices (privacy is a huge concern these days).
Applying AI to business problems
Ways to describe artificial intelligence:
- A set of algorithms,
- That leverage data,
- To provide capabilities,
- That are designed into user experiences and business processes, to create business value.
- Bottom line: a system of technologies operating together to achieve a goal.
What makes AI strong
AI that can learn by itself, and that can achieve superhuman tasks.
Note: we’re not talking about emulating the human mind or performing any tasks that humans can. We’re not trying to create artificial humans.
Commonly used machine learning methods:
- Supervised learning: learning with a clear target label. You can train your models to eventually predict inputs and targets. Example: home prices.
- Deep learning: learning from hierarchical relationships. Each level of a neural network learns levels of features that eventually define what you are trying to predict.
Defining the AI landscape in banking.
- Access to data: Profitability: Cost reduction, revenue gains, and risk reduction.
- Computational power — Competition: arms race with other companies.
- Advanced models: Regulation: reporting, best execution, AML.
Deloitte study: where AI is applied with the greatest impact to banking:
- 65% customer service
- 52% back office/operations
- 42% financial advisors
- 31% fraud detection
- 29% risk management
Offense and Defense for banking customers
- Natural language understanding
- Lead conversion and churn prevention (when customers are unhappy with their service) in sales and marketing.
Vision for the future: the next chapter of where AI might go
One way of looking at it is a digital transformation and a 4th industrial revolution. AI will be:
- Always on.
- Always connected.
- Using natural interfaces that provide contextual experiences.
Additionally, AI can help…
- Update customer locations
- Switch default payment card to business
- Offer Packing advice before travel
- Initiate automatic check-in
- Provide room keys
- Push room number notice
- Order room service
- Order preferences: shades down, music soft.
How AI helps in self-driving cars:
- Check investment performance
- Order groceries
- Pay bills
- Zelle money to…
- Micropay to pass
- eCommerce with delivery to trunk
The fine line among banking and tech and eCommerce is all getting blurred. We’re used to separating these two ideas, but they are no longer considered separate.
How AI helps in customer service:
- Voice-based banking and chatbots
- Personalized website navigation and “intelligent” dashboards
- Natural language search
How AI helps in integrated receivables:
- Automatch incoming payments with invoices
- Reduce manual effort with automation
- Post receivables faster.
How AI helps in banking applications:
- Analyze and compare reports with OCR and NLP
- Cross compare reports in time and content
- Improve reporting accuracy and compliance
Key drivers of risk:
- Accuracy and objective
- Transparency (this is a big deal in banking).
- Bias and ethics: just because we can do certain things does not mean that we should do it.
- Avoid a fear of missing out. Ground yourself in business objectives and values.
- Get a scout. Stay on top of use cases, solutions and trends.
- Build a foundation of AI-readiness with enabling technologies.
- Clarify what AI means to you and your vendors.