Artificial Intellgince — Opportunities for Banking
Written by Sheraz Sharif, Head of Data & Cyber Security at Aion Digital
Artificial Intelligence enables a new perspective to perform business with unique opportunities. This is accomplished by unlocking the value of data to operate intelligently and improve profitability by capitalizing on cutting-edge AI techniques. AI is no longer just an option, we have more data than ever before, we have better algorithms, and we no longer need expensive hardware. As per Forbes, 65% of senior financial management expects positive changes from the use of AI in financial services.
By implementing AI in banking, you can achieve some or all of the following non-exhaustive list;
· Real-time and actionable insights
· Faster, more accurate decision making
· Increase customer engagement & retention
· Increase profit & expense optimization
· Identify revenue-generating streams
· Improved products and service delivery
· Frictionless 24/7 customer service interactions
· Hyper-personalized offerings using predictions and recommendations
The total potential cost savings from AI applications is estimated at $447 billion by 2023 for banks with the front and middle office accounting for $416 billion of that total, as per Business Insider Intelligence. AI can play an integral role in various parts of the financial ecosystem.
The following are some of the most popular examples of AI applications in the Banking & FI sectors.
Know Your Customer (eKYC)
By leveraging optical character recognition (OCR), NLP, computer vision, and data extraction models, banks can find anomalous patterns and identify risks in KYC processes without human intervention. The integration of AI technologies provides benefits like accelerated processing times, improved security and compliance, and reduced errors.
Personalization: Personalization provides clients with a comprehensive solution while reducing the call-centers workload. By using solutions like voice-controlled virtual assistants, smart tools to check balances, schedule payments, look up account activity and more, banks can offer personalized financial advice and help individuals achieve their financial goals. These intelligent systems track income, essential recurring expenses, and spending habits to come up with an optimized plan and financial tips. AI enables banks to develop a stronger relationship with their customers, becoming trusted partners in their lives.
Personal Financial Management: “Autonomous finance — algorithm-driven financial services that make decisions or take action on a customer’s behalf.” Forrester. AI-enabled PFM is opening new possibilities for banks and their customers due to the access to insightful financial and behavioral information. AI is helping PFM platforms by analyzing consumers’ individual account data to see how they’re performing financially, make recommendations, and then automating savings and budgeting for better financial health. AI can also provide insights on spend forecasting, offer customers realistic predictions for smarter decisions, and provide action-based tips and nudges.
Portfolio Management: Virtual Assistants and Robo Advisors can be used to collect information, interpret what is being asked, and supply the answer via fetched data. The AI solution can gradually improve and personalize products based on user preferences. Conversational AI systems can instantly support customers to fulfill their requests. By integrating AI into customer service, their requests are addressed faster, the workload of the call center would be reduced, and they can focus on more complex requests. Robo-advisors specifically have gained significant traction with millennial consumers who don’t need a physical advisor to feel comfortable investing, and who are less able to validate the fees paid to human advisors.
Lending & Credit: Provides a faster more accurate assessment of a potential borrower at a lower cost and accounts for a wider variety of factors leading to a better-informed data-backed decision. The use of AI helps lenders distinguish between high default risk applicants and those who are credit-worthy but lack an extensive credit history.
Risk Management: Increased processing power allows vast amounts of data to be handled in a short time. Adding cognitive computing can help manage both structured and unstructured data, a task that would take far too much time for a human to do. Finally, by using algorithms, banks can analyze the history of risk cases and identify early signs of potential future issues.
Customer Retention/Churn Prediction: Customer churn forecasting is one of the most popular big data use cases in business. It is considered a “churn” when regular customers cancel their subscriptions. AI models can detect patterns in behaviors and predict which customers have a higher potential to churn in the next term. By analyzing these behaviors, banks and other financial institutions can identify why a customer is at risk and take actions accordingly to prevent churn.
Upsell & cross-sell: Banks and other financial institutions can accurately discover unaddressed customer needs thanks to CRM systems and AI technologies. This can help increase customer satisfaction while increasing revenues for the financial institution. AI has changed the way banks look at customer data to make huge strides in being able to offer a unique, personalized offering. Producing tailored recommendations and advertising based on customers' lifestyles and preferences is in line with what they are beginning to expect.
ATM Maintenance: Banks could use AI predictive & prescriptive analytics to know in advance when to send maintenance staff out to ATMs before they become inoperable. This could prevent a bank from losing revenue from ATM fees and allow them to maintain a clientele that would search for other ATMs while the bank is broken. Predictive maintenance capabilities make use of IoT sensors. In this case, banks could attach IoT sensors to various parts of their ATMs.
Identity Verification at ATMs: Banks could install AI-powered facial recognition software into their ATMs that allow for identity verification. This function has gained a lot of traction in China over the last few years, and Chinese companies represent the largest and most well-funded companies offering ID verification at ATMs. SenseTime, for example, has raised over $2.6 billion.
Algorithmic Trading: Intelligent Trading Systems (also called quantitative or high-frequency trading) monitor both structured (databases, spreadsheets, etc.) and unstructured (social media, news, etc.) data in a fraction of the time it would take for people to process it. The predictions for stock performance are more accurate, due to the fact that algorithms can test trading systems based on past data and bring the validation process to a whole new level before pushing it live.
Wealth Management/Investment Banking
There is a colossal amount of data being recorded for assets and it is growing exponentially. By applying AI/ML and automation solutions improves the overall function and decision-making process. Wealth managers and investment bankers could use natural language processing (NLP) for data mining of social media data for research purposes. NLP could scrub the web for news about mergers & acquisitions and look for the sentiment around certain companies to get an idea of how consumers are reacting to them. This could give an idea of which stocks might soar or plummet and allow them to make a more informed decision on what to do with a client’s stocks in the moment — the most prominent technique involves the use of artificial neural networks and algorithms.
Cyber Security: AI-powered Cyber Security applications provides intelligent detection, prevention, and security. Cybersecurity applications are among the most popular AI applications today, this is because these applications rely on anomaly detection which machine learning models are very well suited for. Few top uses-cases within Cyber Security are as follows;
a) Network Threat Analysis monitors all outgoing and incoming calls to detect any suspicious patterns in traffic information.
b) User Behavior Modeling utilizes ML to analyze network traffic information to understand the baseline behavior of each user and device in the firm.
c) AI-based Antivirus Software for identifying abnormal behavior generated by programs rather than syncing known malware signatures.
d) Fighting AI Threats uses AI to safeguard against the threats like wannacry, ransomware, and their risks by using anomaly detection for end-point security in their enterprise networks.
AML/Fraud Detection & Prevention: By using extensive transactional data, AI learns to predict if a transaction is fraudulent or not. AI can analyze a customer’s behavior, location, buying habits, and trigger a security mechanism when something contradicts the established spending pattern.
Process Automation: AI-enabled software verifies data and generates reports according to the given parameters, reviews documents, and extracts information from forms (applications, agreements, etc.). Employing robotic process automation for high-frequency repetitive tasks eliminates the room for human error and allows a financial institution to refocus workforce efforts on processes that require human involvement.
Regulatory Compliance: AI can leverage Natural Language Processing (NLP) technologies to scan legal and regulatory documents for compliance issues. As a result, it is a scalable and cost-effective solution because NLP can be used to browse thousands of documents rapidly to check non-compliant issues without any manual intervention.
Sentiment Analysis: Using machine learning techniques, AI models can predict the market conditions and provide insights into the market trends. Because of this reason, these models are used in hedge fund management functions. Using market trends predicted by AI models, investors can make valuable financial decisions quickly.
News Analysis: AI & Machine Learning (AI) applications understands social media, news trends, and other data sources, not just stock prices and trades. The stock market moves are in response to myriad human-related factors that have nothing to do with ticker symbols, and the hope is that machine learning will be able to replicate and enhance the humans’ “intuition” on financial activity by discovering new trends and telling signals.
AI Document Digitization: Computer vision is used for uploading documents in systems and, allows employees to search for information within those documents. Theoretically, the machine vision algorithm behind document digitization software not only creates a PDF scan of a document but fills in the template of a digital form with the information on each line of that document.
AI Document Summarization: ML and NLP are used to train a computer to simulate an expert’s review of a set of documents. The result is a computer capable of ‘understanding’ the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents, categorize and organize the documents themselves and even update scanned documents.