FinTech industry in Indonesia has grown by 3,055.76% in just 2 years, as stated by the OJK. Top consulting firms such as McKinsey, KPMG and BCG have also reported a similar amount of expansion. The investment made in the industry has increased by 17.25% (or $17,256 billion) since 2014 and more investments are predicted to roll out. Adding to the FinTech’s paramount influence in the financial landscape is the 1200% growth of mobile and internet banking (Source: Macquarie Research), while the dependence to the branch and ATM has declined considerably.
Fortunately, everyone is still early in the game. We have an outspread of untapped markets for P2P or e-wealth management business. 51.1% of Indonesian are unbanked, while 82.8% of Indonesian have never borrowed from any financial institution. Mutual Funds, Bonds, and Shares are the least favoured investment instruments in the affluent SEA households. Moreover, security is a huge issue as CBC Data & OJK showed negative Non-Performing Loan growth, making us 43% away from the government target of 2%.
As scalability remains a concern in Indonesia, this question emerges to be solved: How do we rapidly and securely scale the operations?
Meet Katalis – an integrated core FinTech platform for both lending and wealth management, made to ease your way in implementing E-channel for your financial situation. It is a modular-but-integrated application, which enables it to be independently deployed, customised, and scaled to any business rules. BIT’s proficiency in implementing and maintaining full-scale FinTech operations rooted in our experience in supporting one of Indonesia’s first movers in FinTech business.
*general architecture graphic
AI-POWERED DEBT RECOVERY & CREDIT SCORING
Among other industries, FSI has the second biggest ROI in the AI implementation after Healthcare. Since 2015, AI has demonstrated a terrific growth by surpassing human cognitive ability and accuracy by 1%. In general, AI predicts outcomes based on the ‘recorded behaviour’ to understand the pattern flexibility. Through this understanding, AI can accurately model the problems and operate in any higher dimension possible, allowing it to take more variables than any humans can ever imagine while consuming lesser time to learn.
BIT’s machine learning model has proven to reduce 25% of the non-performing loan (NPL) by keeping 20% promise kept rate. Our lab test provides a credit scoring model with 15% more approval rate, which translates to $7M more business value.
*behavioural science: to enable AI graphic
katalisINVEST – Browse, analyse, buy investment products, monitor the growth of your funds, and break down all investment portfolio you have.
katalisLOAN – Apply for loan or fund other users using flexible pay, instalment, invoice financing; all activities are recorded in the core-financing data services.
katalisCONSOLE – Set up the MLA and Approval matrix and integrate the authentication through active directory.
Model Development – This analytics utilises machine learning to classify customers into multiple categories.
Analytics Tool – Complementing the AI/ML model by allowing interactions for agents and analysts with the Analytics Engine.
Call Center Consulting – Deliver better service through the recommendation on call centre operation procedures this feature provides.