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Data Mining Technique Clustering on Bank Data Set
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Data Mining Technique Clustering on Bank Data Set

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Data Mining Technique Clustering on Bank Data Set by using rapid Miner.X means techniques

Data Mining Technique Clustering on Bank Data Set by using rapid Miner.X means techniques

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  • SR is Leading Non banking finance company <br /> Market Share of 40% commercial vehicle finance in India <br /> 600 branches all over India. <br /> Financing the different commercial vehicle <br /> SGV- Small good Vehicle (4 wheeler only) <br /> LGV- Light good Vehicle (6 wheeler Only). <br /> HGV-Heavy goods Vehicle <br />

Data Mining Technique Clustering on Bank Data Set  Data Mining Technique Clustering on Bank Data Set Presentation Transcript

  • CLUSTERING Presented By: Punit Kishore Arbind Kumar
  • Agenda • • • • • • • Introduction Business problem Analytical problem Analytical solution Business solution Futuristic standpoint Glimpse to process
  • Introduction • • • • XYZ is Leading Non banking finance company Market Share is 40% of commercial vehicle finance in India 600 branches all over India. Deals with different commercial vehicle  Small goods Vehicle (4 wheeler only)  Light goods Vehicle (6 wheeler Only).  Heavy goods Vehicle (Above 6 wheeler only)  Passenger (Commercial only)  Farm equipment
  • Business Problem Due to stagnation in the market of Commercial vehicle and seasonal effect:  Problem 1 - Whom to be focused for Top-up loans/Different Promotional Schemes.  Problem 2 - Whom to be focused for NPA collection/ Refinancing
  • Analytical Problem • • • Portfolio of 1092 customers of the Single Branch. Data contains total attributes of 21 Used Attribute: 8 1) Gross Demand 2) Gross Collection 3) Arrears 4) Arrears Month 5) Advance Amount 6) Agreement Value 7) Settlement figure 8) Loan no
  • Analytical Solution • Group the Customer by using the Data mining Technique - called Clustering. • Tool Used - Rapid Miner • Clustering Technique - X means. • Execution of data in Rapid Miner by using Optimization Process to define the number of clusters and than clustering with X-mean technique • No of Clusters formed - 4
  • Analytical Solution ATTRIBUTES ADVANCE AMOUNT AG VALUE MONTH TBC GROSS_DEMAND GROSS COLL ARREARS ARREARS MONTHS SETTLEMENT FIGURE CLUSTER 0 CLUSTER 1 313878 133241 460158 192076 10322 5480 199968 71292 151388 53064 22973 10616 7 2 287611 120942 CLUSTER 2 755663 1194406 12915 1027523 370450 279245 45 1763839 CLUSTER 3 610505 916074 17391 316964 213065 39026 8 621170
  • Analytical Solution 14000000 40000000 35000000 30000000 25000000 20000000 15000000 10000000 5000000 0 12000000 10000000 gross collection cluster_2 HGVCVMH 8000000 6000000 cluster_2 LGVIGVMGV 4000000 2000000 M I V G L M C V G H R G S A P V G S gross demand 0 cluster_3 1 40000000 35000000 30000000 25000000 20000000 15000000 10000000 5000000 0 M C V G H M I G L V P Q E V M R F R G S A P D L O G P V G S gross demand gross collection cluster_1 2
  • Analytical Solution • Cluster 0  The average loan amount in this set is 3.5 to 4 lakhs  Average preferable Emi is Rs 10132  Average collection is 75%  Comprised of Passenger, Small Goods Vehicle and Light Goods Vehicle • Cluster 1  The average loan amount in this set is 1.5 lakhs  Average preferable Emi is Rs 5438  Average collection is 82%  Mainly Passenger and Small Goods Vehicle
  • Analytical Solution • Cluster 2  The average loan amount in this set is 7.55 lakh  Average preferable Emi is Rs 12915  Average collection is 37%  Comprised of Light Goods Vehicle and Heavy Goods Vehicle • Cluster 3  The average loan amount in this set is 6.10 lakh  Average preferable Emi is Rs 17391.  Average collection is 55%.  Light Goods Vehicle, Heavy Goods Vehicle.
  • Analytical Solution Cluster 1 Cluster 2
  • Business Solution • Cluster 1: It contains 499 customers which should be focused for top up loans/promotional offers- like credit card, tyre loan, engine loan, power loan. Cluster 0 can also be focused for the above but it should be optional call by the Manager. • Cluster 2: The data here is having the average arrears of 45 months, which should be handed over to collection agent and must be forwarded for legal processing. Similarly in Cluster 3, the average loan amount is high and are in arrears so executives can be improvised to maintain the portfolio.
  • Futuristic Standpoint Your text here 3 2 If successfully executed, plan for Pan India Targeting the Cluster 2 to improve the NPA ratio 1 Targeting the Cluster 1 for Business Capitalization
  • Glimpse To Process Cluster Model Cluster 0: 361 items Cluster 1: 499 items Cluster 2: 22 items Cluster 3: 210 items Total number of items: 1092
  • THANK YOU