Fraud Detection and Prevention using Data Science

Righty said Data is the lifeline of a Financial Organization. 90% of the data in the world today is created within the last 2 years. It will likely reach 40.000 exabytes of 40 trillion gigabytes by 2020. A financial company collects a massive amount of crucial and confidential data in terms of customer information, their account details, card details and so on. Managing that data is a complicated process which requires special skill sets and deriving insights from that data is what every organization anticipates.

Today’s high-end technologies make it possible to realize the value of Data. For example, Banks can analyze behavioral trends and social media activity of each customer and provide personalized product offerings. Financial sectors discuss the banks of the future –they generally refer to external things that banks will have in the future. Leveraging Data Science analytics leads to more confident decision making, which means greater operational efficiencies, cost and risk reductions.

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Data science is drawing the attention of all the Financial organizations around the globe and 90% of the organization thinks that successful Data Science initiatives will define them as winners in the future. Financial Services and Banking is leading in applying data science techniques. There are multiple internal data sources in banks - inclusive of databases, Data Warehouses, XML data, enterprise applications like ERP and CRM. Banks also have a large amount of social media data of their customers in the form of tweets, Facebook wall posts, website visits, streams, searches videos, etc.

Banking and Finance industry faces several challenges like-rapidly changing customer requirements, competitive environment, demanding regulatory guidelines, and consumer environment. With all these challenges, financial organizations need to apply Data Science which will help them stay ahead in the competition.

Application of Data Science in Finance & Banking :

MARKETING -Marketing-spend optimization individual messages.

RISK, SERVICING, COLLECTION -Customer-centric risk assessment fraud-prediction assessment early-warning system and tailored offers based on system and collector data.

OPERATIONS -Capacity-planning model network-optimization model identification of service pain-points and simulate trade-offs ATM cash optimization.

SALES & RELATIONSHIP BUILDING -Next product to buy propensity model frontline tools for churn-prediction model.

PRODUCT DESIGN & PRICING -Conjoint analysis for product configuration elasticity modeling for pricing predicting demand modeling.

About the company :Canopus Data Insights provides Data Science and Data Analytics services that enable customer centricity by giving insight into data, which improves productivity and increases the depth of customer engagement and predictive modeling to make big decisions. Canopus Data Insights has an extensive knowledge and experience working with many domains including Data Analytics, Machine Learning, Pattern Recognition, Statistics, Artificial Intelligence, Operations Research and Big Data. Our implementation experience includes working in different sectors like Financial Services, Banking, Insurance, Logistics, Manufacturing, Retail, Marketing.