{"id":60379,"date":"2018-06-15T17:43:03","date_gmt":"2018-06-15T12:13:03","guid":{"rendered":"https:\/\/staging-imarticus.kinsta.cloud\/?p=60379"},"modified":"2022-06-13T10:10:11","modified_gmt":"2022-06-13T10:10:11","slug":"the-importance-of-big-data-analytics-in-the-banking-and-financial-services-industry-data-analytics-blog","status":"publish","type":"post","link":"https:\/\/imarticus.org\/blog\/the-importance-of-big-data-analytics-in-the-banking-and-financial-services-industry-data-analytics-blog\/","title":{"rendered":"The Importance of Big Data Analytics in The Banking and Financial Services Industry"},"content":{"rendered":"

In this data-driven world, Data Analytics has become vital in the decision making processes in the Banking and Financial Services Industry. In Investment banking, volume, as well as the velocity of data, has become very important factors. Big Data Analytics comes into the picture in cases like this when the sheer volume and size of the data is beyond the capability of traditional databases to collect.
\nToday, data analytics practices have made the monitoring and evaluation of vast amounts of client data including personal and security informant data-driven and other financial organizations much simpler.
\nThere are several use cases in which Big Data Analytics<\/a> has contributed significantly to ensure the effective use of data. This data opens up new and exciting opportunities for customer service that can help defend battlegrounds like payments and open up new service and revenue opportunities.
\nFor example, in October 2106, Lloyds Banking Group had become the first European bank to implement Pindrop\u2019s PhoneprintingTM <\/sup>technology for detecting fraud. Their technology used AI to create an \u2018audio fingerprint\u2019 of every call by analyzing over 1300 unique call features \u2013 such as location, background noise, number history, and call type \u2013 the o highlight unusual activity, and identify potential fraud.
\nIt cracks down on tactics like caller ID spoofing, voice distortion, and social engineering without any need for customers to provide additional information. Subsequently, Lloyds Banking Group went on to win the Gold Award for \u2018best risk and fraud management program\u2019 at the European Contact Centre & Customer Service Awards 2017.
\nDanske Bank uses its in-house start-up, advanced analytics to evaluate customer behavior and determine preferences, as well as to better identify fraud while reducing false positives.
\nJPMorgan Chase also developed a proprietary Machine Learning<\/a> algorithm called Contract Intelligence or COiN for analyzing various documentations and extracting important information from them.
\nBig Data is also used for personalized marketing, which targets customers based on the analysis of their individual buying habits. Here, financial services firms can collect data from customers’ social media profiles to figure out their needs through sentiment analysis and then create a credit risk assessment. This can also help establish an automated, accurate and highly personalized customer support service. Big Data also helps in Human Resources management by implementing incentive optimization, attrition modeling, and salary optimization.
\nThe list of use cases implemented in the workflows of the Banking and Financial sector is growing day by day. The huge increase in the amount of data to be analyzed and acted upon in the Banking and Financial Sector<\/strong> has made it essential to incorporate increase the implementation of Big Data Analytics.
\nKnowing the importance of data science is crucial in these sectors and should be integrated into all decision-making processes based on actionable insights from customer data. Big Data is the next step in ensuring highly personalized and secure banking and financial services to improve customer satisfaction.<\/p>\n","protected":false},"excerpt":{"rendered":"

In this data-driven world, Data Analytics has become vital in the decision making processes in the Banking and Financial Services Industry. In Investment banking, volume, as well as the velocity of data, has become very important factors. Big Data Analytics comes into the picture in cases like this when the sheer volume and size of […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_mo_disable_npp":"","_lmt_disableupdate":"no","_lmt_disable":"","footnotes":""},"categories":[23],"tags":[522,649,707,794,831],"class_list":["post-60379","post","type-post","status-publish","format-standard","hentry","category-analytics","tag-career-in-data-analytics","tag-data-analytics-certification","tag-data-analytics-courses","tag-data-analytics-training","tag-data-analytics-career"],"acf":[],"aioseo_notices":[],"modified_by":"Imarticus Learning","_links":{"self":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/60379","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/comments?post=60379"}],"version-history":[{"count":0,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/60379\/revisions"}],"wp:attachment":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/media?parent=60379"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/categories?post=60379"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/tags?post=60379"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}