Machine Learning (ML) is having a big impact on the future of finance particularly the banking industry. It is now a necessity to keep up with competition and banks have realized that big data technologies are now a key dependency because it helps manage resources, make better and smarter decisions and improve efficiency in performance. Its purpose is to drive real results and the future of finance is now being targeted. But why? Efficient customer service is now being served to customers through ML. This has generally surpassed and outperformed conventional approaches such as bank supervisors.
Below represent five different use case studies that demonstrate the applicability of ML within the future of finance and the banking industry.
1. Wells Fargo – Wells Fargo in particular use ML with authentication tools to track
combination patterns that can easily be missed by a human. The new AI Enterprise Solution helps the company to improve insight trends of customer experience to further improve better operational efficiency delivery. The solutions strategy helps to drive customer and team member satisfaction and influence product development. Using advanced API, it helps accelerate company growth and maintain excellent service to corporate banking customers.
2. Fintech Fraud Detection – Service providers regularly experience inbound intrusion
and fraud attacks. FinTech being a prime example, who use ML to perform evaluations with large data sets. The ability to learn results through data sets efficiently determines whether historical data can be labeled as fraudulent or not. The ML algorithms used to perform decisions helps to recognize suspicious activity.
3. Citibank Data Science Start-up – Citibank are well known for introducing start-ups
within financial services and cybersecurity. Their investment within FeedzAI within the data science field helps to detect and remove fraudulent attempts within online and mobile banking. FeedzAI analyses learning algorithms through large data volumes to identify and alert potentially fraudulent activity.
4. Banking Supervision for Information Accuracy (Bank of England) – Bank of
England has invested heavily for the auto inspection of thousands of its records. ML models are trained to supervise banking activity and to accurately detect firm scrutiny through supervisor alerts, an example being scrutinizing balance sheet items and predicting their concern through alerts. These help to take action and guard from large financial penalties, firm equity position and lack of firm sustainability.
5. Virtual Assistants (Bank of America) – Bank of America introduced virtual
assistants, the first established financial company to achieve this some 10 years ago. Erica, the virtual assistant, was introduced to promote innovation through payment and financial services. The assistant performs financial advisor activities to its 45 million customers. Routine transactions completed by a human can now be mirrored by a machine with the aim to improve efficiency and drive better customer experience. Due to its technology transition, mobile banking has benefitted from its improvements with its customer portfolio growing by 10 million within 5 years.
The biggest financial institutions are now understanding the benefits machine learning can provide to their internal operations and customer service. ML has proven to deliver increased efficiency within industrial operations and eliminate human error, heightening the further risk to company reputation and value. Machines will limit these concerns and the future of finance will benefit from larger revenues streams and better brand reputation with individual enterprises.