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Fraud and transactional data af M. Nadeem Gulzar, Danske Bank
Fraud and transactional data af M. Nadeem Gulzar, Danske Bank
Fraud and transactional data af M. Nadeem Gulzar, Danske Bank
Fraud and transactional data af M. Nadeem Gulzar, Danske Bank
Fraud and transactional data af M. Nadeem Gulzar, Danske Bank
Fraud and transactional data af M. Nadeem Gulzar, Danske Bank
Fraud and transactional data af M. Nadeem Gulzar, Danske Bank
Fraud and transactional data af M. Nadeem Gulzar, Danske Bank
Fraud and transactional data af M. Nadeem Gulzar, Danske Bank
Fraud and transactional data af M. Nadeem Gulzar, Danske Bank
Fraud and transactional data af M. Nadeem Gulzar, Danske Bank
Fraud and transactional data af M. Nadeem Gulzar, Danske Bank
Fraud and transactional data af M. Nadeem Gulzar, Danske Bank
Fraud and transactional data af M. Nadeem Gulzar, Danske Bank
Fraud and transactional data af M. Nadeem Gulzar, Danske Bank
Fraud and transactional data af M. Nadeem Gulzar, Danske Bank
Fraud and transactional data af M. Nadeem Gulzar, Danske Bank
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Fraud and transactional data af M. Nadeem Gulzar, Danske Bank

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Oplægget blev holdt ved InfinIT-arrangementet Big Data og data-intensive systemer i Danmark, der blev af holdt en 15. januar 2014. Læs mere om arrangementet her: …

Oplægget blev holdt ved InfinIT-arrangementet Big Data og data-intensive systemer i Danmark, der blev af holdt en 15. januar 2014. Læs mere om arrangementet her: http://infinit.dk/dk/arrangementer/tidligere_arrangementer/big_data_i_danmark.htm

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  1. Fraud and transactional data Updated January 12th , 2014 Author: M. Nadeem Gulzar
  2. M. Nadeem Gulzar • • Chief Architect, Risk Management IT at Danske Bank • • • • Approx. 2100 people, more than 2000 systems Education: • M.Sc. from Copenhagen University in Computer Science and Mathematics Employed in Danske Bank Group since: 2003 Experience Summary: • 14+ years of IT experience, primarily within software development and middle management • Former jobs first as IT-developer and Department manager 2
  3. Agenda 1 Mind setting 2 Use of Big Data in Real world scenarios 3 Additional use of Big Data 4 Questions 3
  4. Agenda 1 Mind setting 2 Use of Big Data in Real world scenarios 3 Additional use of Big Data 4 Questions 4
  5. Mind setting • In the modern financial world, customers are served through numerous channels going from simple face-to-face dealings to mobile/tablet banking. This requires an increased focus on automatically detecting potential fraud to avoid any unnecessary loses. • One approach to this is to utilize the transactional data available to detect patterns and behavior of individual customers. 5
  6. Agenda 1 Mind setting 2 Use of Big Data in Real world scenarios 3 Additional use of Big Data 4 Questions 6
  7. Use of Big Data in Real world scenarios • Scenario 1 • • Russian hackers utilize a botnet to make small transactions across numerous accounts Main points • Involved customers are completely unaware of the fact that their computer is hacked and part of this • Many customers will not report anything due to the small amounts involved • What do we do today • • Today our fraud detection team will manually handle this based on some reports they produce What can we do with Big Data • • • Monitor transactions not only on an individual account level, but at portfolio level as well ”Buckets” can be created on different levels each with specified thresholds Back office receives alerts based on the defined patterns 7
  8. Use of Big Data in Real world scenarios • Scenario 2 • • Karen is an ordinary woman with a family, job etc. One day she is on her way to work, when she decides to get a cup of coffee from the local gas station • After the purchase, she inadvertently drops her credit card and continues off to work • Mr. X comes along, picks up the credit card and decides to abuse it online • Later in the evening Karen is on her way back from work, realizes that her credit card is missing and reports it • Main points • • Customers do not report loss of credit cards etc. instantly There are many ways to abuse a credit card without the pin code 8
  9. Use of Big Data in Real world scenarios • What do we do today • • Today we will not react until contacted by the customer, which implies loss for the bank What can we do with Big Data • • Having monitored Karen's transactional data historically, we identify her shopping patterns Abnormalities to those identified patterns are registered and alerts are sent to Karen on her mobile (false alerts could be generated) • Karen would need to approve prior to the transaction taking place • False alerts are marked and patterns are dynamically adjusted (if chosen by Karen) 9
  10. Use of Big Data in Real world scenarios • Scenario 3 • • • • • • • • • • • Meet Michael He is married to Linda and together they have 2 children (Magnus and Lucas) They live in a 3 million DKR house with a standard 30-year mortgage loan Michael is a sales director Linda is a stay at home mom They are both financially liable for their common economy At a Christmas party Michael hooks up with Christina Michael is quite fond of Christina, so he starts sending her flowers Michael is not ready for conflicts, so he sends flowers to Linda at the same time Michael applies for a loan of 100.000 DKR to cover gifts and trips with Christina Michael later applies for a larger loan to cover the down payment for Christina’s new apartment under the false pretence that Magnus will be moving out and needs an apartment • A year passes, Linda finds out about Christina and files for a divorce • All the loans are split between the 2 parties, but both parties are unable to pay 10
  11. Use of Big Data in Real world scenarios • Main points • • • • What do we do today • • • Michael’s transactional behavior changes when he meets Christina He orders 2 sets of flowers, 2 sets of necklaces etc. Could we predict the divorce? If the economy of the household can sustain additional loans, we simply approve them Divorce and split of property is not considered What can we do with Big Data • Any information gathered of this sort should only be used internally and it should be considered whether we even should use it • Transactional history of customers getting a divorce (with adultery being main cause) could reveal a pattern • Michael and Linda’s advisor will receive an alert as soon as Michael’s transaction pattern changes • Advisor could then ask Michael if he needs further financial advice given the situation he is in 11
  12. Agenda 1 Mind setting 2 Use of Big Data in Real world scenarios 3 Additional use of Big Data 4 Questions 12
  13. Additional use of Big Data • Machine learning • Machine learning disciplines (random forest and support vector) can be used to predict the probability of whether a customer would default or not • Should machines make credit decisions? • Property Valuation (borderline “Big” Data) • • • • • • • • Calculate prices of all properties in Denmark Use the entire neighborhood (recently sold properties) to calculate Use of demographic data Use of BBR data How many renovations have happened on the property How many people search for properties in this area People buying property in this area, where do they live today Etc. 13
  14. Agenda 1 Mind setting 2 Use of Big Data in Real world scenarios 3 Additional use of Big Data 4 Questions 14
  15. Questions ? 15
  16. Thank You 16
  17. Disclaimer • Legal • • Using transactional data is not permitted in all countries, ie. in Denmark the Persondatalov does not allow the use of transactional data in such a manner that you can extract information for operational use. Ongoing project • Danske Bank is in the process of building a fraud system based on transactional data, so what you see today are some of our thoughts 17

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