In the 1990s, the introduction of non-branch banking brought about a digital revolution with major concerns around security. Initially viewed as an insecure way of banking, within a decade, online banking has not only increased customer convenience and satisfaction but has also help saved costs and increase penetration for the banks.

Banks have moved from information to insight, security being top priority. Let's take the example of the very backbone of inter branch banking that is the Core Banking Systems(CBS) which has made banking extremely convenient but micro transactions at the same time adds to the load on the core system leading to issues like delay and security concerns. OBOPAY's layered banking solution aims at reducing the load on the CBS at the same time encouraging volumes and growth in micro transactions. Such technology clearly sends a message to the customers to confidently use digital banking services and helps bank in establishing systems that are more reliable. This is only possible since banks are open to adapting to evolving technologies and are actively investing in IT infrastructure.

Advancements in information management systems such as ma-chine learning and artificial intelligence have paved way for cloud first strategy business model. Artificial intelligence has helped banks like ICICI deploy robots in hundreds of business functions which have led to reduction in customer response time by up to 60

percent. SBI has an AI solution which scans cameras installed at bank branches, captures facial expressions and gives a real time feedback whether customers are happy or not. Chat bots are classical example of artificial intelligence, put to use across banking and other sec-tors. Banks need to actively engage in putting AI to use to help customers make investment choices, reduce response time, automate various tedious, repetitive and time consuming functions. Major Banks world-wide are already working on various AI innovations and are receptive to technological advancements.

Machine learning plays a vital role in the Banking/Fintech ecosystem and needs to be actively used in portfolio management, automated trading, fraud detection, sentiment analysis to name a few

IOT and connected devices are expelling large quantities of data for consumption by advanced analytics solutions. Machine learning plays a vital role in the Banking/Fintech eco-system and needs to be actively used in portfolio management, automated trading, fraud detection, sentiment analysis to name a few. Roboadvisor is nothing but a simple algorithm based on machine learning that helps create financial portfolio basis the user's investment goals and risk appetite. The widespread acceptance of roboadvisors across the financial industry is a proof of readiness to take the next technological leap with futuristic algorithms promising to solve complex automation needs. Since data is at the heart of automation, banks need to focus on investing in robust machine learning solutions.

Block chain, another buzz word in the financial world is a technology that banks need to watch out for. Intra bank cross border transfers, corporate payments, cross border remittance are the areas banks should focus on. Presently banks are keener in adopting block chain technology for intra bank cross border transfers. If this transformative technology is fully adopted, apart from helping to process payments quickly it would also aid in reducing cost, quicker settlements and lead to fewer errors and exceptions. In order to capitalize on the block chain technology banks need to invest in building a truly global network defined by standards and easy on boarding process.

Banks are moving at a rapid speed to embrace technology and continuous investments are being made to stay ahead in the race. The banks need to be vigilant enough in infrastructure implementation as any misses would lead to loss in huge amounts and customer trust.