Support　Vector　Data　Description　(SVDD)　has　a　limitation　for　dealing　with　a　large　dataset　or　online　learning　tasks.　This　work　investigates　the　practice　of　credit　scoring　and　proposes　a　new　incremental　learning　algorithm　for　SVDD　based　on　Karush-Kuhn-Tucker　(KKT)　conditions　and　convex　hull.　Convex　hull　and　part　of　newly　added　samples　which　violates　KKT　conditions　are　treated　as　new　training　samples　instead　of　previous　support　vector　and　entire　new　arrived　samples.　The　proposed　method　can　achieve　comparable　training　time　with　traditional　incremental　learning　algorithm　for　SVDD　while　have　similar　classification　accuracy　with　original　SVDD.