Different Ways In Which AI Will Revolutionise Financial Services Sector
Robotic Process Automation (RPA), one of the newer AI technologies, is enabling banks develop more efficient workflow systems
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It was recently reported that Alipay, the world's biggest payment company, had hit $100 billion in transactions in less than a year with zero branches. Traditional banks would have taken multiple years to achieve this milestone. The financial services landscape is changing rapidly and technology is playing a pivotal role in catalyzing this transformation. Innovations in Artificial Intelligence (AI), the Internet of Things (IoT) and Blockchain technologies are disrupting the world of banking and finance more than ever.
The underpinning need for Artificial Intelligence, especially in the financial services sector, is being acutely felt today. AI has evolved way beyond chatbots answering simple customer queries. Advanced analytics and AI can now be applied to every aspect of banking-from real time customer engagement, to more efficient operational processing to better risk and fraud management. This is because AI spans across not just one, but a spectrum of technologies such as big data, language processing, deep learning, machine learning, robotics, and facial, speech and gesture recognition. Listed below are different ways in which AI can be used to unlock potential in the financial services sector.
Provide greater customer insights
Customer insights are invaluable to the banking sector. PayPal is using deep learning to discover payment fraud, while JP Morgan Chase is using machine learning for fund flow analytics. Credit Suisse, Deutsche Bank and Goldman Sachs have deployed machine learning in their high frequency trading platforms. Several banks are also leveraging AI technologies to track corporate accounts, and for automated sanctions compliance management.
Such advances are possible because of the already available technologies, which AI stitches together. While big data tools mine the available repository of data, machine learning technologies can dig deep into the insights to establish predictive patterns.
Create proactive customer engagement
As day to day interactions become increasingly digitalised, banks and financial organisations are using various AI technologies to design customer-centric experiences. DBS is currently leveraging Natural Language Processing (NLP) to review customer chat logs to enhance the quality of customer interactions. ICICI and HDFC are testing out similar NLP techniques to generate research advisories for customers who are keen on wealth management solutions. AI enabled innovations such as 'Selfie Pay' that are being piloted by Mastercard in North America are making use of video/image analytics to deliver optimal value to today's digitised customer.
Transform cyber security and fraud detection
As the recent ransomware WannaCry demonstrated, cyber security is a huge threat to banks and financial organisations. Ironically, there is an increase in the number of AI-enabled cyberattacks, creating the need for banks to invest in better AI systems. Companies such as Sun Financial and Sumitomo Mitsui Banking Corporation are currently testing IBM's artificial intelligence system Watson to identify, and stop malware programs. Since the AI system enables data to be anonymously fed into the AI system, banks are more willing to participate and share data.
Graph Analytics is another AI technology that is being pioneered in fraud detection in the banking sector. Goldman Sachs is presently using this new vertical in AI to create an automated compliance and fraud analytics system. Anti-money laundering systems based on AI machine learning technologies will also make it easier to spot illegal or unethical sources of financial transactions.
Develop more efficient workflow systems
Robotic Process Automation (RPA), one of the newer AI technologies, is enabling banks develop more efficient workflow systems. Wells Fargo is making use of RPA for mortgage processing and reconciliations. ICICI automates reconciliation of ATM declined transactions and disputes with AI powered RPA solutions. Goldman Sachs uses RPA for market reconciliation, while JP Morgan Chase has automated trade ledger reconciliations for Forex account and ledger management. More importantly, AI technologies have the ability to maintain compliance with both existing and emerging regulations. The bottom line of leveraging AI in the financial sector is the massive savings that AI technologies can bring. For instance, when AI technologies were used to interpret loan agreements, JP Morgan Chase saved 360,000 hours a year.
The financial services sector today is trying to cope up with increased competition, reduced profitability and higher liquidity regulation requirements, amongst others. In light of all this, the future of the banking and financial services sector lies in the timely adoption of AI technologies.
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