Project objectives

• Modernizing the existing decisioning infrastructure and automating the approval process
• Having the ability to set up product parameters and configuration easily
• Empowering the business users to do & deploy the changes without the need of IT

Why has ALJ Finance Egypt chosen TaranDM

• The team – Taran has experienced team which covers expertise from credit risk, antifraud, modelling and scorecard areas. We do not usually see such well-rounded support during pre- and post-launch, not only from technical infrastructure and interface perspectives, but Taran team also accommodated our inquiries from the business strategy areas
• The product – TaranDM has refined technology stack and offered features which are highly relevant in addressing pain points from credit underwriting capabilities perspectives

Implementation highlights

• Automation of the underwriting processes which have been mostly manual in the old system
• Integration with several data source and ability to consume them in the real time
• The Egyptian Credit Bureau (iscore)
• Behavioral data
• Internal blacklists
• Onsite training to ensure the smooth knowledge transfer to client’s team. The business users are now self-sufficient and are able to modify data input & output, create new business logic or change the existing one, test it and deploy to production by themselves

AREA42’s objectives

• Overarching goal: improve international trade by creating new and innovative technological solutions and a fluid, risk-savvy B2B trade ecosystem
• To achieve that, AREA42 has been looking for a decision engine with the following features:
• Ability to make quick, completely automated decisions
• Well-designed GUI for business users
• Modern technology stack and features such as easy data integrations, model deployment, out-of-the-box simulation layer, etc.

Why has AREA42 chosen TaranDM?

• Simple and flexible deployment of scoring models, including the support of multiple analytical environments for the model development
• Both real-time and batch decisioning-making and processing capabilities
• Easy maintenance of the decision engine
• Modern technology stack & “Python-native” nature of TaranDM
• Taran team’s credit risk and data science domain expertise which has ensured smooth & well-executed TaranDM implementation

Implementation highlights

• Development of a credit bureau adapter
• Deployment of the TaranDM Antifraud module:
• In-built data source which stores & automatically calculates concentration checks, cross checks and other aggregations which are crucial for fraud prevention
• Examples: number of loan applications from the same IP address over past 1h/24h/7d/30d; number of loan applications from the same device over past 1h/24h/7d/30d; etc.

Neo Financial’s adjudication goals

• Replace the existing credit decisioning infrastructure with an enterprise decision engine which allows a high level of flexibility to credit risk managers in designing and testing multiple adjudication strategies and processes simultaneously
• Allow for faster time-to-market when deploying new decisioning logic or its changes
• Eliminate dependency on engineering and developer teams and give credit risk managers the ability to have end-to-end ownership of their processes

Why has Neo Financial chosen TaranDM

• Platform-as-a-service” way of delivery – the decision engine runs at the client’s infrastructure (either cloud or on-prem), no sensitive data is shared with the vendor (customers’ personal details, etc.)
• Streamlined implementation & process migration, measured in weeks, and not in many months or years
• Modern technology stack – containerized, Python-based decision engine with microservices architecture
• Multiple out-of-the-box functionalities usually not available in other decision engines, such as Impact Report (simulation of decisioning logic’s changes against the status quo), batch decisioning, or Decision Optimizer

Implementation highlights

• TaranDM implemented and Neo Financial’s existing credit adjudication process migrated in less than 3 months
• Continuous knowledge transfer during the implementation & phase-in, along with TaranDM’s general customizability, has ensured that Neo Financial’s internal teams will not be overly dependent on us as the vendor going forward
• Higher flexibility when deploying new decisioning logic, for example:
• Credit product pricing now determined by the strategy and client historical profile/credit bureau record
• Specific strategies for selected partners which allow fine-tuning of approval rates/risk profiles and support of marketing actions and rollout plans
• Seamless A/B testing of strategies for various client segments
• Includes hundreds of pre-defined aggregations and it’s customizable further (client can create additional aggregations)

Business objectives

• Leveraging the group's existing branch network and building a bank with modern technologies & fully cloud-based infrastructure
• Finding a decision engine which can be used for various products & decisioning processes with all degrees of complexity

Why has Partners Banka chosen TaranDM

• Modern technology stack, good fit for our digital banking infrastructure
• Features & comprehensiveness of the solution
• Responsiveness, focus on our needs, and overall approach from the vendor
• Great references

Project highlights

• Implementation of complex decisioning workflows, which:
• combine automated decisions with manual checks, verifications and requests for additional documents
• fetch data from more than 10 external data sources, such as the credit bureau, insolvency & debtor registers, foreclosure & distraint records, open banking / PSD2, document fraud detection and several real estate appraisal systems
• automatically assess the creditworthiness of the applicant and also the co-applicants (if any) and generate multiple tailored offers
• Knowledge transition to the client's team, which ensures that the risk & business users are now able to change credit policy, offer parameters and data sources prioritization by themselves without the need for IT involvement

Migration from a legacy decision engine

• RinggitPlus had been using a decision engine from a large vendor for several years
• The solution wasn’t flexible enough, it was technologically out-of-date & required support from the vendor
• As a consequence, it had been significantly hindering RinggitPlus business growth. Moreover, it was very expensive

Why has RinggitPlus chosen TaranDM

• Tremendous flexibility for the business users & risk managers. No dependence on IT/engineering
• Modern technology stack. Decisioning logic versioned, archived, timestamped, attached to specific user & can be easily deployed or roll-backed
• IT security & compliance aspects. TaranDM runs on client’s infrastructure (cloud/on-premise). Vendor doesn’t store or have access to any production data
• Great references from the existing clients

Migration scope

• 2 strategies (credit card, personal loan)
• 3 stages (pre-bureau, post-bureau, offer optimization)
• Close to 50 policy rules
• 5 scorecards
• 8 decision trees/decision tables

Implementation approach & highlights

• The RinggitPlus was concerned that the migration will take as long as the initial setup of the legacy engine (> 6 months)
• However, the design of decisioning strategies in TaranDM is significantly more efficient & flexible than in traditional decision engines. For example:
• Long decision trees are replaced with decision tables, which are easier to prepare & maintain
• Scorecards can be uploaded in one click, no need to manually configure binning and transformations
• Decisioning logic shared by multiple strategies can be easily re-used via so-called components. No copy pasting or manual re-creation is needed
• <span class='font-weight-bold text-primary'>Ultimately, the implementation has been successfully finished within ca 2 months, from the very start of the project to migrating 100% of the traffic through TaranDM</span>

Silk Bank’s objectives

Launching digital lending products with fully automated approval process

• Modernizing the bank’s credit risk decisioning process and infrastructure
• Ability to launch any new configuration of decision-making components in days

Why has Silk Bank chosen TaranDM

• Silk Bank chose TaranDM for its role in modernizing credit decisions and launching digital lending products. TaranDM's intelligent features ensure compliance and enhance financial precision. Beyond its impact on the approval process, TaranDM's ability to seamlessly integrate with various data sources positions it as a versatile solution for Silk Bank, ensuring a holistic and informed approach to decision-making. The platform will significantly improve our approval process by providing tailored product offers, backed by thorough credit bureau analysis. Its seamless integration with data sources, such as CreditBureau and the Revenue Service, underscores TaranDM as a comprehensive solution for Silk Bank. The platform's adaptability and robust features make it an instrumental asset in Silk Bank's journey towards modernized and efficient financial operations.
• In choosing TaranDM for our bank, we are leveraging a proven solution that has consistently delivered beyond expectations for reputable institutions. The client stories from TaranDM previous projects underscore the ability to not only meet but exceed strict timelines and facilitate the seamless migration of credit assessment processes. What distinguishes TaranDM is its adaptable deployment, showcased in prior project instances where it facilitated real-time data collection and operated seamlessly in a platform-as-a-service framework. This adaptability aligns with Silk Bank's need for a cutting-edge solution that integrates seamlessly with diverse data sources, empowering us to make informed decisions in real time. Additionally, TaranDM's commitment to knowledge transfer and customization ensures Silk Bank's teams are self-sufficient and not overly dependent on external vendors, which is a crucial aspect for long-term operational success. Additionally, TaranDM offers a user-friendly interface and a no-code graphical platform, empowering Silk Bank's team to adapt quickly to changing market demands and configure strategies independently. Its real-time processing capabilities ensure a swift and responsive customer experience, aligning seamlessly with Silk Bank's commitment to efficiency and customer satisfaction.
• In essence, TaranDM is not just a tool, it’s a strategic ally in our pursuit of excellence, combining cutting-edge data science with a user-friendly interface to drive innovation, efficiency, and superior customer satisfaction in Silk Bank's financial operations. This partnership positions Silk Bank ahead in the banking industry, empowering us to navigate the evolving landscape with agility and stay ahead in delivering unparalleled financial services.

Project highlights

• Implementation of the sophisticated income estimation logic to be compliant with the regulatory/internal requirements
• Enhancement of the overall approval process:
• TaranDM generates all product offers for which the customer is eligible
• That includes a refinancing offer, if relevant based on the credit bureau information
• Successful and continuous know-how transition to the Silk Bank’s team. Well before the production launch, they configured 2nd loan product by themselves in TaranDM’s intuitive, no-code GUI
• Easily integrating the approval process with several data sources, like Credit Bureau, Revenue Service, National Bureau of Enforcement, and National Bank of Georgia

Migration from a legacy decision engine

• Tonik Bank had been using a decision engine from a traditional vendor for several years and decided to change it for a modern “cloud born” solution fully hosted within the bank infra- structure

Why has Tonik Bank chosen TaranDM

• Taran offers a tool enabling simple, efficient and flexible control of the approval processes, including seamless integration of external data sources
• Taran showed very pro-client based approach but was also very clear, realistic and honest in terms of implementation timeline, and did not make any unrealizable promises

Implementation highlights

• The implementation of TaranDM & full production migration from the legacy infrastructure took a few months, including the launch of 2 credit products (1 configured by Taran team, 1 configu- red by Tonik Bank team themselves in TaranDM’s GUI)
• Data orchestration (workflow management) has been migrated from existing Loan Origination System to TaranDM
• Tonik Bank team can now see & control their end-to-end decisioning flow (all stages, including data source calls) in TaranDM GUI