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Lambda Architecture-Ideal for Healthcare Big Data Use Cases
Shahid Shah, Co Founder and CEO, Netspective Communications
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Brett MacLaren, VP, Enterprise Analytics, Sharp HealthCare
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Derek Wilson, President & CEO, CDO Advisors LLC
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Anne Legg, VP - Strategic Marketing, Credit Union Solutions, Fiserv
Artificial Intelligence and Machine Learning: Managing the Risk Factor in Finance
“Too much of anything is good for nothing,”—the wisdom in this adage relates best to today’s most hyped cutting edge technology, Artificial Intelligence (AI). Everything around us from personal virtual assistant to smart cars is today powered by AI. This intuitive technology has also made inroads into financial institutions such as banks. AI makes information processing more efficient and it also helps in improved customer interactions in a cost effective manner. However, AI can also pose risk if it is not implemented and monitored properly. According to the Financial Stability Board (FSB) report, financial institutions that are increasingly using AI and machine learning across the system in a large number of applications must ensure foolproof security.
Banks, insurance companies are replacing humans with computer systems which might give way for cybercriminals to manipulate market prices, making business operations more vulnerable to targeted attacks. The other key concern for financial institutions is the confusion pertaining to how the computer systems and robots will react during the time of economic breakdown and other related forms of crisis.
By eliminating the human factor from decision and judgement making, financial institutions are eventually shifting toward higher risk by depending more on machine learning. With the increasing number of cyber crimes, these risks may prove to be damaging if AI and machine learning are used for ‘mission-critical’ applications in the financial institutions. Therefore, it is necessary for financial institutions to understand all the implications related to AI and machine learning applications, and must take proactive and preventive measures to avert the untoward impact in the areas of data privacy and cybersecurity.