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Malaysia Bank Adopts Artificial Intelligence Early Warning System to Reduce Risks of Non-Performing Loans (NPLs)

The client is one of the largest international banks, headquartered in Singapore with a prominent presence in the Asian region. As a Financial Institution with significant focus on commercial loans, the challenge of managing credit risk from loans turning delinquent is pervasive and a major concern to the bank. With a clear understanding that high NPL ratios demand greater loan provisions, which reduces capital resources available for lending and dents the bank profitability, the Bank decided to embark in this journey to mitigate the risks of NPLs.

The Bank was using manual excel-based spreadsheets to forecast potential delinquencies based on historical data and repayment behaviors. This manual process was labor intensive, time consuming, lacking futuristic prescriptive analytics modeling and its precision was questionable. Scalability of analysis was a constant challenge with changing economic climate and trends.

Pounded by these challenges, the Bank realized its need for an automated and intelligent solution with the capability to provide early warning for delinquent accounts.

Solution

With a significant double-digit increase in the number of approved loans, the Bank prioritized its focus on effective management of loans recovery and delinquencies. The Bank realized the need for automation coupled with deployment of Artificial Intelligence (AI) instead of the labor intensive manual processes which lent a toil on the credit management teams.

The conventional CRISP-DM (Cross Industry Standard Process for Data Modelling) breaks the process of data mining into six major phases of which the sequence of the phases is not strict and moving back and forth between different phases is always required. This conventional model usually requires disparate tools and trained statisticians to navigate usage of these tools for data mining.

Juris Technologies has taken a distinctive forward-looking approach in bringing Machine Learning to the next level.  Leveraging design thinking and embedment of wizards, the business users are empowered with the power of machine learning and artificial intelligence to apply their own domain knowledge and hypothesis in training the different analytical models.  By empowering business users, organizations reap the benefits of significant improvements with the capabilities of continuous training of analytical models.

The Bank engaged Juris Technologies, an end-to-end broad spectrum customer and credit management solutions provider to automate workflow and processes for loans management. With precise understanding of the Bank’s need, Juris Technologies proposed and delivered the Juris Mindcraft solution, a system powered by Artificial Intelligence capabilities, to predict potential Non-Performing accounts as early as 6 months prior to delinquency.

Juris Technologies utilized the Bank’s readily available data, consolidated various attributes of Term Loan, Trade and Overdraft services of accounts, and applied Artificial Intelligence prescriptive models. These models combined historical accounts behavioral analysis with future predictive analytics and algorithms to identify and segregate accounts potential of deterioration to NPL status. In addition to analytics, the solution also provided the Bank’s users with insights, rationale and rules for the identification of such accounts, enabling strategic and tactical actions to be deployed in managing and reducing NPLs.

Benefits Obtained

Combining the capabilities of the Bank’s proprietary Credit Scoring Model with Juris Technologies’ early warning and account monitoring system, the Bank successfully enhanced its existing credit assessment structures and risk assessment capabilities. Coupled with improved economic climate, the Bank was able to reduce NPLs significantly from a sharp hike to a plateau.

With reduction in NPLs, the Bank enjoyed increased profitability and improved their ability to provide new lending and a healthy working capital.

Industry: Banking

Big Data Analytics Solutions ProviderJuris Technologies is a Malaysia-based, MSC Status company providing innovative business solutions in the financial services and telecommunications industry.

Juris Technologies started business operations in 2003 with a primary focus in building solutions serving the Banking, Financial Services and Insurance (BFSI) and Telecommunications industry. Our solution consists of an integrated framework of scalable components which is used to build enterprise quality solutions.

Our solution framework is used to design and develop solutions for customer relationship management with modules such as Contact Management, Campaign Management, and Sales Force Management. In the areas of Credit Management, we provide solutions for Customer Acquisition, Loans Origination, Credit Scoring, Conveyancing and Litigation, and Collection and Recovery. Today, we are the leading customer and credit management solutions provider in Malaysia, offering a collaboration platform for over 1,000 external parties including legal firms, collection agencies and valuers.

Our key clients are Affin Bank, Agro Bank, Alliance Bank, Bank Kerjasama Rakyat Malaysia (BKRM), Bank Persatuan, Bank Simpanan Nasional, CIMB Bank, CTOS, Hitachi Capital, HSBC, JCL, Maybank Etiqa, OCBC Bank, SME Bank, Standard Chartered Bank and Tenaga Nasional Berhad (TNB) Malaysia.

Our global footprint for the licensing of Juris Application Server spans across 22 countries and  the Juris Application Server framework is used by organizations such as Sony USA and The Software Engineering Institute of Carnegie Mellon.

Singapore Bank Embraces Machine Learning To Boost Their Advertising Campaigns

The client is one of the largest banking and financial services corporations in Asia. Headquartered in Singapore, the conglomerate has operations across multiple countries around the region.

The bank has a substantial budget for digital ad campaigns. In the past, however, a large portion of their digital advertisements were not optimized; their ads were reaching many viewers who either had no interest in their products or lacked the propensity to purchase anything from the bank.

In fact, if we look at the current digital ad landscape, 54% of digital advertising in general goes unseen, resulting in wasted impressions and opportunities. Marketing channels as well as audience are highly disjointed in this digital age. Audience fragmentation has made it nearly impossible for marketers to manually segment their target market and perform targeted selling. Also, if they were to miss out key segmentation variables or channels, opportunities to convert would be lost.

The bank realized that there was a need to optimize their ads – automatically and in a very intelligent manner.

Solution: The opportunity for the bank was to continuously find the best target audience for their ad campaigns. And this process had to be done automatically instead of manually tweaking the hundreds of parameters staring at the marketing team in the face.

The bank engaged Optimate, a campaign optimization engine that uses self-learning artificial intelligence (AI) to automate and enhance their digital advertising projects. Optimate has a delicately-designed AI system that uses data-driven logic, learning from historical campaigns and matching promoted ads and products with interest keywords in their proprietary knowledgebase.

Centered on the bank’s desired target market, as well as content from ad creatives, the system would dynamically search for the right audience. Optimate applied sampling-based methodology to augment the scale of data, and to generate the most appropriate target audience based on evidence provided by the data itself.

Optimate AI Overview

The ad optimizing engine was also able to diagnose the status of the client’s ad account via a Competitiveness Index (CI) from all the data within the account linked to their system. A/B testing automatically carried out on different ad creatives by the self-learning algorithms before being pushed to the target audience.

Further budget adjustment analysis was also provided to the bank to complete the system’s set of automatic ad campaign creation and optimization solutions.

Benefits & ROI: The bank’s overall ad effectiveness improved tremendously; the AI engine perpetually worked in the background to fine-tune and deliver the ads to the right audience, all in real-time.

In the two campaigns that were optimized by Optimate’s AI engine, the bank managed to achieve up to 36% increase in reach, 48% reduction in cost per link click, 41% reduction in cost per post engagement, and 50% increase in relevance score.

In a short span of 4 days, the bank managed to cut down their campaign cost by 20%.

And the good ROI will only get better as more campaigns are run for longer periods of time. The more valuable data is generated and analyzed by the AI engine, the more effective the ad optimizations will be.


Did you miss last week’s Optimate Webinar on the Future of Marketing 2017? You can view the recording here. The webinar covered common FB marketing mistakes, 2017 marketing trends, and how SMEs can tap into AI-driven, automated marketing tools for runaway marketing success.

As someone who runs digital ad campaigns, you stand to benefit from Optimate’s sophisticated AI marketing engine. You can sign up for one of their plans using the discount code OPTDECWEB.

Industry: Banking
Big Data Analytics Solutions Provider: Optimate is an AI optimization engine for multiple channels of marketing. Combining self-learning artificial intelligence (AI) to automate and optimize online marketing with a cloud platform, Optimate enhances businesses’ marketing activities by streamlining the process of serving the best content to suitable audiences using machine learning. Optimate now serves over 100 local and regional clients, ranging from Fortune 500 companies to local SMEs. www.optimate.co

How a Malaysian bank used data science to increase its credit card spend

Client Profile: The bank is one of the largest financial institutions in Malaysia with retail branches in numerous Southeast Asia countries.

Challenge: A substantial segment of the bank’s credit card holders with high spending potential is not utilizing its credit card as their primary card.

Solution: Cardholders with spending potential were segmented based on various demographic attributes such as gender, product holdings and amount spend.

Collaborative filtering – a technique to predict user interest based on the behavior of other users – was applied to detect spending patterns. For example, the bank wanted to know how frequently merchant offerings appeared against customers with specific product holdings. And how often merchant offerings showed up against customers of a particular demographic.

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Customer segmentation and collaborative filtering to forecast consumer propensity scores

Each potential customer was scored on their spending propensity for every merchant category like dining, petrol, groceries and airlines.

The bank then matched specific merchant offers to individual customers based on their inclination to spend in that merchant group.

Customer data, card transactions and the effectiveness of previous merchant offers were used to identify potential cardholders to drive higher usage of the bank’s credit cards.

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Every customer is scored against every merchant category on their propensity to spend

Business Benefits: With personalized marketing campaigns derived from data analytics, the bank saw an increase in its credit card spend and utilization, resulting in a substantial increase in interchange and interest income.

Industry: Banking

Big Data Solutions Provider: Teradata Malaysia
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