APAC CIOOutlook

Advertise

with us

  • Technologies
      • Artificial Intelligence
      • Big Data
      • Blockchain
      • Cloud
      • Digital Transformation
      • Internet of Things
      • Low Code No Code
      • MarTech
      • Mobile Application
      • Security
      • Software Testing
      • Wireless
  • Industries
      • E-Commerce
      • Education
      • Logistics
      • Retail
      • Supply Chain
      • Travel and Hospitality
  • Platforms
      • Microsoft
      • Salesforce
      • SAP
  • Solutions
      • Business Intelligence
      • Cognitive
      • Contact Center
      • CRM
      • Cyber Security
      • Data Center
      • Gamification
      • Procurement
      • Smart City
      • Workflow
  • Home
  • CXO Insights
  • CIO Views
  • Vendors
  • News
  • Conferences
  • Whitepapers
  • Newsletter
  • Awards
Apac
  • Artificial Intelligence

    Big Data

    Blockchain

    Cloud

    Digital Transformation

    Internet of Things

    Low Code No Code

    MarTech

    Mobile Application

    Security

    Software Testing

    Wireless

  • E-Commerce

    Education

    Logistics

    Retail

    Supply Chain

    Travel and Hospitality

  • Microsoft

    Salesforce

    SAP

  • Business Intelligence

    Cognitive

    Contact Center

    CRM

    Cyber Security

    Data Center

    Gamification

    Procurement

    Smart City

    Workflow

Menu
    • Banking
    • Cyber Security
    • Hotel Management
    • Workflow
    • E-Commerce
    • Business Intelligence
    • MORE
    #

    Apac CIOOutlook Weekly Brief

    ×

    Be first to read the latest tech news, Industry Leader's Insights, and CIO interviews of medium and large enterprises exclusively from Apac CIOOutlook

    Subscribe

    loading

    THANK YOU FOR SUBSCRIBING

    • Home
    Editor's Pick (1 - 4 of 8)
    left
    Will Finance Automation Prove to be the Merger of the COO & CFO Roles?

    Amit Agrawal, Delivery Manager, NTT Data

    Asian Development Bank: Using Emerging Digital Technologies for the Common Good

    Shirin Hamid, CIO, & CTO, Asian Development Bank

    An In-depth Sight on Banking Technology

    Sandeep Khera, Chief Information Officer, XAC Bank

    Where was the Cloud when I was Younger?

    John Ferlito, Chief Technical Officer, Bulletproof

    Synergistic Opportunities for Banks and Fintech Companies

    Patrick Maes, CTO,

    Data Analytics: Bringing In A New Level Of Detail And Visibility

    David H. Robinson, SVP & Chief Information Officer, Lockton

    Demonstrating Business Value is Key To Success

    Mark Schlesinger, SVP & CIO, Broadridge

    Why Your Payments Strategy is the Key to Your Digital Future

    Tina Giorgio, President and CEO, ICBA Bancard

    right

    Financial Service's Battle Against Fraudulent Activities Using Big Data Analytics

    Fandhy H. Siregar, CISA, CRISC, CGEIT, CISM, CISSP, CIA, CRMA, CCSA (Works As Chief Audit Executive, An Experienced Auditor And Enthusiasts In Cybersecurity)

    Tweet
    content-image

    Fandhy H. Siregar, CISA, CRISC, CGEIT, CISM, CISSP, CIA, CRMA, CCSA (Works As Chief Audit Executive, An Experienced Auditor And Enthusiasts In Cybersecurity)

    Combating financial crime can no longer be an acceptable cost of doing business. At the same time, financial service produces billions of data from their activities every day. Financial service institution is operating in highly regulated industry whereby protection of customer assets is essential as there are also complex compliance requirements to be adhered with. Financial products are becoming more complex which transactions were digitized as the global economy also shifted to online-based system. All of these inevitabilities are possible because of the presence of modern technology. Moreover, the availability of modern technology has enabled fraudulent techniques to become more sophisticated, however technological advances also allow better ability to detect and prevent fraud.

    The old method of responding to fraud right after the fact is unsound in this new world of sophisticated financial crimes. Financial institutions are also moving to cloud computing, accelerated mobile applications and enterprise social media, which can add to the vulnerabilities. Nowadays, financial service institutions are becoming more adaptive in dealing with large volume of data. However, it is not only the reason why we call it ‘Big Data’. The amount of data isn’t necessarily that important, the ability to combine existing data from various origins with public data such as social media, websites, and blogs is what this type of solution becoming more exceptional. Analyzing the volatility of data is also an important factor to be considered in designing the capability of dealing with real-time data access.

    Big data requires a multi-layered implementation approach. The big data system is made up of three tiers that include data, integration, and visualization and analytics. The latter will provide information for decision making process and can be utilized as detection control. Typically, the fraud investigation team relies on a data analyst team to perform a search on data warehouses using SQL queries that store large amounts of transaction data, customer data, and other information. Because of the size, volatility, and variety of data stored in the data warehouse, this search process can take a long time before enough evidence and sufficient in the investigation and prosecution process. The longer it takes to detect fraud, the more difficult it is to uncover the fraud, in addition, the more detrimental for the institution.

    Basically, there are five essential components of effective anti-fraud program

    The longer it takes to detect fraud, the more difficult it is to uncover the fraud, in addition, the more detrimental for the institution

    a) Prevention: Improve internal controls to prevent fraud.

    b) Detection: Predict the fraud before it occurs.

    c) Responding: Applying an understanding of the latest fraud mode.

    d) Investigation: Conduct intelligence fraud to look for causes and offenders.

    e) Continuous Improvement: Make use of historical fraud data that has occurred for the purpose of continuous anti-fraud cycles.

    In this paper, we will focus on the second component, Detection. This fraud detection system can be designed once and used many times using the data analytic tool. Given the amount of data that investigators need to filter out to find the pattern of fraud, a massive data and a viable searching system is the most feasible approach. Information sourced from the public is collected and processed through a large data framework, afterward a process of data cleansing and normalization process are conducted before distributing data to multiple data-mart/store. The processed data is also entered into the search engine. The predictive analysis system works to show the fraud indicators and proactively detects suspicious activity. Usually these red-flags or fraud indicators have been developed and coded into the predictive analysis program and executes according to pre-determined schedule to generate an exception/indication report. This report will become a source for further analysis by fraud analyst/investigator. A search-based graphical interface is also provided to researchers for analysis and documentation of evidence.

    Furthermore, leveraging information from other sources such as conversation notes, email correspondences, and social media, are among the benefits of a big data approach, whereby unstructured sources are combined with official (structured) data. Identifying hidden relationships through network analysis and data correlation are further enhanced data analytic methods. Imagine that you can find a potential conflict of interest by looking at the similarity of geographical location of vendor/third-party address with you employee address.

    Complex data architecture enables fraud detection efforts in financial services institutions to become more measurable, faster, and more accurate. Because the system actually processes and analyzes any existing data, fraud analysts also give more confidence to their discoveries. Indicators have been built in a continuously approach whereby a new knowledge of fraudulent activity mode will enhance the current predictive analysis program or create new indicator along the life-cycle of the fraud detection system. At this point of time, it may requires human intervention to renew or enhance the analytical program. Moreover, if it has ability to automatically learn and improve from experience without being explicitly programmed, it will allow to another milestone in building the adaptive model. The approach that is commonly known as Machine Learning with regards to fifth/last component above.

    And the rise of big data analytics tools like ones which developed as open-source software such as Apache Hadoop® for massive data distribution and Apache SparkTM for massive data processing tool, or using cloud-repositories like Microsoft Azure as your infrastructure as a service (IaaS), will allow more powerful implementation of this adaptive and robust solutions in the future. Another skill to handle a programmable audit tool is also necessary while the basic ingredient of this skill set is indeed a knowledge of business acumen.

    The maturity of fraud risk awareness in your institution is also a significant factor to have successful implementation. A voluntary whistleblowing system will enrich information gathering on potential misconducts and improper behaviours based on volunteer observation. Together, they will build a complete anti-fraud program and improve the chance of winning the battle against fraudulent activities.

    tag

    Financial

    Big Data

    Cloud Computing

    Hadoop

    Data Warehouse

    Machine Learning

    Weekly Brief

    loading
    Top 10 Banking Technology Companies - 2020
    ON THE DECK

    Banking 2020

    I agree We use cookies on this website to enhance your user experience. By clicking any link on this page you are giving your consent for us to set cookies. More info

    Read Also

    From Friction to Function: How Winc Turned Customer Feedback into Business Growth

    From Friction to Function: How Winc Turned Customer Feedback into Business Growth

    Cara Pring, Digital & Cx Director, Winc Australia
    Why Contact Centres are Becoming Strategic Hubs for Social Insight

    Why Contact Centres are Becoming Strategic Hubs for Social Insight

    Cindy Chaimowitz, GM Wholesale & Customer Service and Karen Smith, Head of Customer Service, Foodstuffs North Island
    Why Compliance Needs a Seat at the Strategy Table

    Why Compliance Needs a Seat at the Strategy Table

    David Koh, Head, Legal & Compliance (Singapore) and Operational Risk Management Country Lead, Perpetual Limited
    Streamlining Operations and Empowering Teams in Facilities Management

    Streamlining Operations and Empowering Teams in Facilities Management

    Shaye Rogers, Workflow Support Manager, Cushman & Wakefield
    Technocreativity: The Synergy Of Technology And Creativity

    Technocreativity: The Synergy Of Technology And Creativity

    Tran Nguyen Phi Long, Group Head Of Retail Marketing, Pnj Group
    Leading It And Digital Transformation At Ikea: Insights From An Industry Veteran

    Leading It And Digital Transformation At Ikea: Insights From An Industry Veteran

    Sigit Triwibowo, Head Of It And Digital, Chief Technology And Digital, Ikea
    Executive Leadership And Digital Transformation In The Global Fashion Industry

    Executive Leadership And Digital Transformation In The Global Fashion Industry

    Eiko Ando, E-Commerce And Digital Director, Pvh Corporation
    Digital Transformation in Fashion Retail - From Efficiency to Experience

    Digital Transformation in Fashion Retail - From Efficiency to Experience

    Le Van, CTO, YODY Fashion
    Loading...
    Copyright © 2025 APAC CIOOutlook. All rights reserved. Registration on or use of this site constitutes acceptance of our Terms of Use and Privacy and Anti Spam Policy 

    Home |  CXO Insights |   Whitepapers |   Subscribe |   Conferences |   Sitemaps |   About us |   Advertise with us |   Editorial Policy |   Feedback Policy |  

    follow on linkedinfollow on twitter follow on rss
    This content is copyright protected

    However, if you would like to share the information in this article, you may use the link below:

    https://banking.apacciooutlook.com/cxoinsights/financial-services-battle-against-fraudulent-activities-using-big-data-analytics-nwid-5124.html