Artificial intelligence is a new approach to information discovery and decision-making. Inspired by the way the human brain processes information, draws conclusions and codifies instincts and experiences into learning, it is able to bridge the gap between the intent of big data and the reality of practical decision-making. Artificial Intelligence (AI), machine learning systems, and natural language processing are now no longer experimental concepts but potential business disrupters that can drive insights to aid real-time decision making. Each week there are new advancements, new technologies, new applications, and new opportunities in AI. It’s inspiring, but also overwhelming. That’s why I created this guide to help you keep pace with all these exciting developments. Whether you’re currently employed in the banking industry, working with Produvia, or just pursuing an interest in the subject, there will always be something here to inspire you.
Today, banks and financial servicing companies must embrace artificial intelligence technologies in order to improve business engagement, automation, insights, and strategies.
AI Ideas for Banking
There are many opportunities for artificial intelligence in the banking industry. Here are a few AI ideas to consider:
Intelligent Mortgage Loan Approvals
Imagine a technology that pulls third-party data to verify an applicant’s identity, determines whether the bank can offer pre-approval on the basis of a partial application, estimates property value, creates document files for title validation and flood certificate searches, determines loan terms on the basis of risk scoring, develops a strategy to improve conversation, provides real-time text and voice support via chatbot. (BCG, 2017) Imagine a system that approves mortgage loans by comparing the applicant’s finances with data for existing loan holders. Imagine software that calculates mortgage risk based on a wide range of loan-level characteristics at origination (credit score, loan-to-value ratio, product type, and features), as well as a number of variables describing loan performance (e.g., number of times delinquent in the past year), several time-varying factors that describe the economic conditions a borrower faces, including both local variables such as housing prices, average incomes, and foreclosure rates at the zip code level, as well as national-level variables such as mortgage rates. (Justin Sirignano, 2016)
- Risk Management
Imagine software that gains intelligence from various data sources such as credit scores, financial data, spending patterns. (FinExtra, 2017) Imagine a technology that identifies a risk score of a customer based on his or her nationality, occupation, salary range, experience, an industry he or she works for, and credit history. (Quora, 2017)
- Fraud Detection
Imagine a technology that establishes patterns based on the historical behavior of account owners. When uncharacteristic transactions occur, an alert is generated indicating the possibility of fraud. (FinExtra, 2017) Imagine software that can detect fraudulent patterns by analyzing historical transaction data. (Feedzai, Nymi, Zoloz, Biocatch)
Imagine a system that detects suspicious transactions, voice recognition software that confirms the identity of a bank customer whose credit card information has been stolen, and cognitive-automation technology that recommends an action — perhaps via a chatbot — to that customer. (GCG, 2017) Imagine software that detects financial fraud using anomaly detection.
Credit Risk Management
Imagine software that allows for more accurate, instant credit decisions by analyzing news and business networks. This system can also be used to improve Early Warning Systems (EWS) and to provide mitigation recommendations. (Accenture, 2017)
Risk and Finance Reporting
Imagine Robotic Process Automation (RPA) which allows a business to map out simple, rule-based processes and have a computer carry them out on their behalf. Imagine a program that reads and understands unstructured data or text and makes subjective decisions in response, similar to a human. This system enables banks to meet regulatory reporting requirements at speed, whilst reducing costs. (Accenture, 2017)
- Customer Service Chatbot
Imagine a banking chatbot that understands customer behavior, tracks spending patterns, and tailors recommendations on how to manage finances. Imagine a chatbot that helps customers perform routine banking transactions while offering simple insights on improving finance management. Imagine a bot that curates targeted offers and promotes relevant products and services, thereby increasing customer satisfaction. (FinExtra, 2017)
- Customer Engagement
Imagine a technology that improves customer understanding and activation through personalization, influencing desired actions. (Deloitte, 2017)
- Banking Automation
Imagine software that automates repetitive, knowledge & natural language-rich, human-intensive decision processes. (Deloitte, 2017)
- Banking Insights
Imagine a technology that determines key patterns and relationships from billions of data sources in real-time to derive deep and actionable insights. (Deloitte, 2017)
- Shape Strategies
Imagine software that builds a deep understanding of the company, market dynamics, and disruptive trends to shape strategies. (Deloitte, 2017)
Predict Cash at ATM
Imagine an algorithm that predicts the cash required at each of its ATMs across the country, combining this with route-optimization techniques to save money. (McKinsey, 2017)
Detect Anti-Money Laundering (AML) Activity
Imagine a technology that detects anti-money laundering (AML) activity by tracing the true source of money and identifying disguised illegal cash flow. (FinExtra, 2017)
Imagine technology provides continuous monitoring of transactions and is able to better identify if a particular transaction is worthy of follow up investigation, given the system's analytics of historical transaction patterns and behaviors. (Medium, 2017)
Practical AI In Banking
There are many banks that are now incorporating artificial intelligence technologies. Here are a few of our favorites:
In Europe, more than a dozen banks have replaced older statistical- modeling approaches with machine-learning techniques and, in some cases, experienced 10 percent increases in sales of new products, 20 percent savings in capital expenditures, 20 percent increases in cash collections, and 20 percent declines in churn. The banks have achieved these gains by devising new recommendation engines for clients in retailing and in small and medium-sized companies. They have also built microtargeted models that more accurately forecast who will cancel service or default on their loans, and how best to intervene. (McKinsey, 2015)
In Canada, a major Canadian Bank reduced watch list checks from 12 hours to less than 15 minutes, increased name-checks from 2,500 to more than 40,000, reduced false positives by 75%, and realized ROI in 3 months. (IBM, 2017)
A South American Bank improved efficiency by 60% by reducing administrative costs. They also reduced AML alerts by 90% which in turn increased accuracy by 60%. (IBM, 2017)
Do you work in the financial services industry? Are you interested in developing artificial intelligence technologies to solve banking problems?
Schedule a call with Slava Kurilyak, Founder/CEO at Produvia.