Technology Disruption & Its Impact In Banking & Financial Services
Sachin, a proficient manager, an avid speaker, and a keen strategist. He has vast experience in leading multiple teams for running successful finance operations & experience of developing standard, industrializing processes for business excellence. He is self-starter in penetrating innovative ideas in managing independent operations & ensuring optimal utilization of resources with reporting to multiple senior stakeholders in matrix structured organisations.
Let's try to understand this subject with a small story or a situation Peter is a customer of a leading bank with a Mortgage, Checking, Savings and a line of credit. Peter is now remodelling his kitchen and is looking to buy a set of Cutlery. The bank tracks spending patterns and has seen that Peter has made lot of household purchases recently. The bank further analyzes his data in terms of his Income, Savings, Credit, Spend habits, Credit and Risk score. The bank also analyzes and tracks his behavior on social media and finds that, on Google plus, he loves to cook; on LinkedIn, he loves to visit new restaurants; and on Facebook that he loves blogging on dining experiences and is looking to buy a six ring gas stove.
The bank now wants to offer him a credit card; but before that, it calculates his predictive spending and offers him a new line of credit worth USD 2500 to buy gas stove at zero percent interest rate. Peter uses this credit to buy gas stove. The bank then offers an insurance product with extended warranty for gas stove.
Next day, the bank offers 20 percent cashback on lunch to Peter. It also offers additional discount if he shares their offer to five more people. Peter uses the cash back and spends on his credit card. The bank sends him alerts to check fraudulent transaction. Peter then logs into his account where a Robot greets him and takes him to My offers page, where Peter gets special offers on home credit, overdraft and others. A spend manager section is also provided to Peter to analyze his spend habits and change if needed.
This is the story that is happening in most of the Financial services and Banking industry since technology is providing cross selling and up selling opportunities for banks to emerge powerful and profitable than before.
Lets understand the key technologies that impact the financial services industry, especially Banking:
•Block chain:Can be used in Treasury management, FX transfers, KYC and many others.
•Robotics Process Automation:Automate work using robots for recording and reconciliation.
•AI & Speech Recognition:Track customer interactions and make meaningful analysis for decision making.
•AI & Machine Learning:Track transaction patterns in large data sets, can be useful in Anti Money laundering, Wealth management and other areas.
•AI & Natural Language Recognition:Track customer interactions and make meaningful analysis.
•Biometrics:Can be useful in KYC and mobile security.
•Virtual Agents:These are basically bots/chat bots to interact with customer. Bank can leverage these technologies to become:
More Collaborative: The world of APIs has brought this concept of Connected/ Collaborative banking, where banks need to connect with multiple platforms to provide straight through processing For example, payments are recorded directly in customers ERP based on payment API. On front desk, portfolio management services can be completely automated with decision making using machine learning with large data set analysis.
More Profitable:Data analytics and speech recognition can help provide cross-selling and upselling opportunities as in the case of Peter above by analyzing customer behaviours and spend habits. Chat bots with Natural Language Processing are creating micro moments for customers to give a personalized experience by understating their needs for banking relationship.
Smart Bank: RPA is being used to automate mundane work of recording and reconciliation especially in areas like treasury, teller management and reconciliation, with branches. Similarly, chat bots are used to solve customer queries on bank balances. Biometrics are heavily used to recognize customers and also ensure compliance. Mobile and smart wallets are another use of technology to improve customer experience in payments industry.
Data Driven Compliance: Compliance and risk management can be automated and made robust using AI, machine learning and behavior tracking. Block chain can be useful in KYC and money transfers with complete audit trail. Machine learning can help detect risks and potential frauds in money laundering. Finally, AI and Block chain can be used to deliver legal compliance to Central banks using complex workflows for central bank reporting.
Key challenges that banks face in implementing these technologies are:
Data Quality: The data quality with single source of truth remains the most important challenge. All the above can be provided only if the quality of data that is available for analysis is right and accurate.
Systems Landscape: Banks are still on old on premise mainframe systems which may not be easy to collaborate with external systems. The challenge to break the existing complex landscape and adopt Hybrid cloud or bolt on solutions to provide better customer experience remains a challenge for big banks.
Talent: Availability of right talent in this field of disruptive technology continues to be the biggest challenge.
Disruptive Startups: Threat of `being ubered' remains from emerging startups like Paytm or Google Pay, where banks need to adopt and react fast in the fast changing world.
Regulations:Being a highly regulated industry, financial services executives feel it difficult to implement new technologies due to uncertain regulations that may change in future.
The Way Forward for Banking & Financial Services Industry
Technology will definitely bring value to all stakeholders in the industry: Customer with personalized experience; Employees with new opportunities; and Shareholders & Management with reduced cost and improved profits. The industry should have a calculated approach towards adopting new technologies by having a prioritization matrix based on the current and future needs. The industry should also drive this transformation based on outcome/customer impact they want to achieve rather than get carried away with peer pressure or matching the competition. Employee and Union concerns are equally important, and talent management will become the foremost pillar on this transformation journey. Also, Data cleansing and having a role of chief data officer would be very important before carrying-out any transformations.
Let's try to understand this subject with a small story or a situation Peter is a customer of a leading bank with a Mortgage, Checking, Savings and a line of credit. Peter is now remodelling his kitchen and is looking to buy a set of Cutlery. The bank tracks spending patterns and has seen that Peter has made lot of household purchases recently. The bank further analyzes his data in terms of his Income, Savings, Credit, Spend habits, Credit and Risk score. The bank also analyzes and tracks his behavior on social media and finds that, on Google plus, he loves to cook; on LinkedIn, he loves to visit new restaurants; and on Facebook that he loves blogging on dining experiences and is looking to buy a six ring gas stove.
The bank now wants to offer him a credit card; but before that, it calculates his predictive spending and offers him a new line of credit worth USD 2500 to buy gas stove at zero percent interest rate. Peter uses this credit to buy gas stove. The bank then offers an insurance product with extended warranty for gas stove.
The BFS industry should have a calculated approach towards adopting new technologies by having a prioritization matrix based on the current and future needs
Next day, the bank offers 20 percent cashback on lunch to Peter. It also offers additional discount if he shares their offer to five more people. Peter uses the cash back and spends on his credit card. The bank sends him alerts to check fraudulent transaction. Peter then logs into his account where a Robot greets him and takes him to My offers page, where Peter gets special offers on home credit, overdraft and others. A spend manager section is also provided to Peter to analyze his spend habits and change if needed.
This is the story that is happening in most of the Financial services and Banking industry since technology is providing cross selling and up selling opportunities for banks to emerge powerful and profitable than before.
Lets understand the key technologies that impact the financial services industry, especially Banking:
•Block chain:Can be used in Treasury management, FX transfers, KYC and many others.
•Robotics Process Automation:Automate work using robots for recording and reconciliation.
•AI & Speech Recognition:Track customer interactions and make meaningful analysis for decision making.
•AI & Machine Learning:Track transaction patterns in large data sets, can be useful in Anti Money laundering, Wealth management and other areas.
•AI & Natural Language Recognition:Track customer interactions and make meaningful analysis.
•Biometrics:Can be useful in KYC and mobile security.
•Virtual Agents:These are basically bots/chat bots to interact with customer. Bank can leverage these technologies to become:
More Collaborative: The world of APIs has brought this concept of Connected/ Collaborative banking, where banks need to connect with multiple platforms to provide straight through processing For example, payments are recorded directly in customers ERP based on payment API. On front desk, portfolio management services can be completely automated with decision making using machine learning with large data set analysis.
More Profitable:Data analytics and speech recognition can help provide cross-selling and upselling opportunities as in the case of Peter above by analyzing customer behaviours and spend habits. Chat bots with Natural Language Processing are creating micro moments for customers to give a personalized experience by understating their needs for banking relationship.
Smart Bank: RPA is being used to automate mundane work of recording and reconciliation especially in areas like treasury, teller management and reconciliation, with branches. Similarly, chat bots are used to solve customer queries on bank balances. Biometrics are heavily used to recognize customers and also ensure compliance. Mobile and smart wallets are another use of technology to improve customer experience in payments industry.
Data Driven Compliance: Compliance and risk management can be automated and made robust using AI, machine learning and behavior tracking. Block chain can be useful in KYC and money transfers with complete audit trail. Machine learning can help detect risks and potential frauds in money laundering. Finally, AI and Block chain can be used to deliver legal compliance to Central banks using complex workflows for central bank reporting.
Key challenges that banks face in implementing these technologies are:
Data Quality: The data quality with single source of truth remains the most important challenge. All the above can be provided only if the quality of data that is available for analysis is right and accurate.
Systems Landscape: Banks are still on old on premise mainframe systems which may not be easy to collaborate with external systems. The challenge to break the existing complex landscape and adopt Hybrid cloud or bolt on solutions to provide better customer experience remains a challenge for big banks.
Talent: Availability of right talent in this field of disruptive technology continues to be the biggest challenge.
Disruptive Startups: Threat of `being ubered' remains from emerging startups like Paytm or Google Pay, where banks need to adopt and react fast in the fast changing world.
Regulations:Being a highly regulated industry, financial services executives feel it difficult to implement new technologies due to uncertain regulations that may change in future.
The Way Forward for Banking & Financial Services Industry
Technology will definitely bring value to all stakeholders in the industry: Customer with personalized experience; Employees with new opportunities; and Shareholders & Management with reduced cost and improved profits. The industry should have a calculated approach towards adopting new technologies by having a prioritization matrix based on the current and future needs. The industry should also drive this transformation based on outcome/customer impact they want to achieve rather than get carried away with peer pressure or matching the competition. Employee and Union concerns are equally important, and talent management will become the foremost pillar on this transformation journey. Also, Data cleansing and having a role of chief data officer would be very important before carrying-out any transformations.
