Providing services instantly, 24x7 and through a growing number of channels while minimizing transactions costs and ensuring safety is key to banks' success.
Banking happens in an environment where massive amounts of data is generated in real time, and where each transaction needs to monitored for risk from the perspectives of customer safety, regulatory compliance and banks' own exposure. While AI offers promise to simplify these tasks, the 24x7 monitoring needs, ever changing macro regulations to comply with, and newer services that customers demand, make it challenging to organize and execute on competing dimensions, while keeping within stretched operational budgets.
Autonomous systems are able to stay in always-on scan mode, while monitoring on risk parameters on a far larger scale than possible with traditional systems while being intelligent enough to uncover emerging risk parameters and broad patterns across geos and time periods.
Delivering differential product experience to customers through deep learning that can understand the customer proactively
Deliver personalized customer experience through all interactions
Use Autonomous AI systems to design customer specific intelligence that can manage customer lifecycle right from targeting to sustenance thus delivering a differential experience
Personalize target strategy that can understand personas to achieve better conversion rates
Understand customer requirements from product viewpoint and align product experience, and even provide inputs for new product
Personalize various elements of interaction such as product choice, pricing, and even documentation required for processing or
Improve transaction monitoring efficiency through flagging risks in real time and with high capture and hit rates
Monitor transaction risks across channels autonomously
Automate the transaction monitoring strategy that would consider thousands of dimensions, beyond human assigned rules, to monitor in real time and flag risks with high precision and recall
Detect anomalous behaviour in real time and map against known and yet undiscovered fraud triggers
Go beyond rigid and rapidly dated rule based triggers through deep learning system that learns independently and stays current without explicit new rules
Achieve high precision and recall through systems that apply intelligence to each case rather than overarching rules that conflict
Detect complex and emerging threats in real time and be able to intervene effectively in a timely manner
Efficiently monitor trade transactions through lower effort towards risk and compliance with high recall on identifying transgressions.
Manage compliance risks associated with trade finance
Provide comprehensive coverage on transactions through expertise based on historical data, risk and regulatory policies and independent outlier spotting and trend discovery abilities
Filters out known restrictions vis-a-vis commodities, entities, counterparties, ports intelligently managing variations and 'system
Spot outliers and discern trends across many dimensions probably beyond human cognition to flag risk factors
Provide data driven insights to human experts thus increasing their efficiency for complex cases that need investigation
Ability to take full historical context across transactions into account while analysing individual transactions to develop a powerful lens
Automate mundane 'cost' tasks with high efficiency thus improving the bottom line and delivery TAT and improving customer experience.
Automate paper based transactions while boosting efficiency and quality
Using Deep learning based vision and reasoning systems to automate transactions thereby handle higher volumes, decrease human involvement and deliver quicker turnarounds.
Automate processes that have underlying paper or image based data by extracting key information points and then reasoning on these points combined with policies
For complex cases, augment experts by through insights providing focus on key triggers or query for further information
Scale up systems to handle peak loads, e.g. spikes in ecommerce transactions driven by holiday season, without needing to augment temporary staff
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