Services Overview

Intenda Financial Services is a modern FinTech company with a strong Analytics Team consisting of actuaries, data scientists and software development and data professionals with expertise in business intelligence, economics, finance, software development and data analysis.

Intenda Financial Services is a subsidiary of the Intenda Group the creators of the fraXses, an end to end data fabric platform.

Our mission is to empower our clients with the tools required to gain insight and understanding into their businesses and that of their clients.

Our team specializes in creating customized business intelligence and management information system solutions utilizing progressive enabling technologies that objectively measure the performance of the business under investigation.

Data visualization technologies creates an easy-to-use interactive environments and dashboards where data can be visually explored and summarized for greater insight empowering our clients to make informed business decisions faster and allows management to objectively measure key success measures.

Company data is mined and analyzed to expose hidden patterns and trends, thereby identifying business opportunities and threats.

Our solutions utilize software which are readily available within most organizations or which can be acquired at a reasonable cost.

Businesses have different information requirements and system environments and therefore we follow a flexible approach in customizing our product according to each individual client’s needs and specifications.

By integrating data from multiple sources such as legacy systems, call centers, web logs, payroll and HR systems, project management and financial data, we can provide customers with a complete picture of all aspects of their business.

Our business solutions simplify business intelligence and extends its availability to everyone in an organization.  Powerful applications within the model can be created in a short period of time.

Our Business Intelligence and Decision Support solution
can fast-track the achievement of the following objectives:

  • Accurate, up to date and complete information that allows executives and employees to make better decisions.
  • Reduced decision times leading to increased responsiveness.
  • Improved operational efficiency.
  • Operational areas that are empowered with information and insight to reach their personal and divisional targets
  • Improved line of sight of individual teams as to their contribution to the overall business objectives.
  • Continuous performance measurement, that allows clients to make timeous adjustments to problem areas.

Services Completed

Use Cases

1. Selected Client Projects

Customer is an emerging pharmaceutical distribution company. Vendor created a bespoke demand planning and forecasting tool to achieve the following objectives

  • Aggregate the stock on hand and historic outward orders and sales to various wholesalers from several distribution centres / warehouses (supply).
  • National pharmacy orders imported from a market data aggregator (demand).
  • Obtain the current list of forward orders and deliveries (national and international) from the customer’s accounting system.
  • Allow customer management of stock objectives per product including minimum order quantity, reordering levels and stock cover levels desired.
  • Projection of future ordering points and estimated stock levels.
  • Ability to modify the suggested sales demand in consultation with the marketing department and save different scenarios.
  • Scenario comparison tool for difference scenarios in the same months as well as current month to previous months.

The solution reduced total monthly demand planning effort from a week to less than a day.

Customer is an emerging pharmaceutical distribution company.

Vendor created a sales tracker tool showing month to date sales (accumulating daily) from several sources compared to sales targets per product and region. 

Vendor is in the process of prototyping a sales/order optimizer for the following use case: “Sales manager needs to focus efforts of their sales representatives around the high value customers for which orders are still expected in the current month, but for which no order have been received yet.”

The solution uses an Artificial Neural Network (ANN) to learn the historic sales behavior of customers assigned to each sales representative in terms of each of the company’s top products.

High touch/value clients and top products were derived through a once-off statistical data analysis.

Customers were also segmented into customers with regular versus irregular ordering patterns. 

The solution shows a probabilistic distribution of the total expected sales orders per client and product for the current month per week.

Expected orders predicted are then compared to the actual orders made on the company’s ordering system for the month.

Expected orders not yet fulfilled are signaled to the respective sales representatives through the personal sales performance dashboard of reach sales representative and the relevant regional sales manager as potential short-term opportunities.

The recommendations are also compared to the individual sales representative’s customer call activity logs to verify that a suitable level of engagement is maintained and that the recommendations are followed up.

The ultimate objective of the solution will be to predict the latent stock on hand of each pharmacy client and their regional demand.

Customer is one of the largest medical scheme administrators in South Africa with five major medical schemes under its administration.

Vendor implemented automated machine learning technologies combined with a centralised data warehousing and reporting solution to

  • A multi-tiered detection system as part of their internal audit infrastructure deployed monthly.
  • The system is primarily concerned with detecting anomaly or outlier claims for fraud or human error investigations.
  • Gender specific claim modelling. Identifying potentially fraudulent claims in conjunction with claim classification systems capturing unusual fringe case events with implications for medical service providers.
  • Modelling and discovery of hospital claims code usage. Identifying incorrectly filed and potentially fraudulent claims.

Funds recovered for this system exceeds R4 million, last estimate taken in August of 2019. Recovered funds are expected to exceed this amount.

Customer is a large government medical pre-funding scheme with several pensioners for whom medical services are provided.

Vendor performs and annual valuation of the assets and liabilities of the scheme to check the financial health and funding levels of the scheme.

The financial projection involves a 60-year projection of the schemes expected cash-flows taking the following into account

  • The age and gender of each existing member.
  • The profile of new members joining the scheme.
  • The contributions of each tier of membership (depending of age at entry and level in the parent organisation)
  • Resignation and mortality rates of contributing members
  • Claim prevalence and claim amounts per incident for pensioners.

The solution also allows the evaluation of the financial impact of several different proposed interventions to improve the scheme’s financial position including

  • Increasing contributions or varying the contribution payment patterns for new and existing members
  • The impact on the scheme of the COVID-19 pandemic.
  • Various claims interventions such as reference price lists, creating a network of designated service providers, disease and chronic condition management interventions.

The solution provides ongoing management intelligence to the board of trustees improving their decision-making ability.

Customer is a large government medical pre-funding scheme.

Vendor managed to obtain and geo-code the geographic location of all members and potential service providers nationally. The providers were grouped and described relative to the nature of the services they provide. This information was displayed on an interactive dashboard for the customer conveying the following information

  • The nearest provider providing a certain type of service.
  • The total number of members surrounding a provider.
  • The extent to which each provider was engaging with the current membership.
  • Identify irregular behaviour with respect to out of region visits of members to providers.

The customer was able to use this information to

  • evaluate the accessibility of its current preferred provider network to need the needs of most members in the region.
  • Add new providers to the network based on regional accessibility especially in rural and outlying regions.
  • Consolidate the provider network in large urban areas to the providers willing to negotiate preferential rates.
  • Improve the data quality of member address information.

The solution is maintained for the purpose of an annual review of the provider network arrangements.

Customer is a large South African order fulfilment and logistics company in the technology and mobile sector who utilizes local logistic partners as well as in-house delivery mechanisms.

Vendor created a consolidated data warehouse containing all operational data together with a dashboard view using a leading reporting framework of the customer’s choice.

Vendor also used the same data to illustrate the total customer experience index (perception of time from order to successful delivery) and demonstrated ways in which a focus on fulfilment optimization can achieve a better customer experience and reduce the number of repeated or failed delivery attempts.

The project also highlighted the need to better data integration between the customer and their courier and delivery partners.

Revenue loss due to failed delivery attempts and subsequent redelivery is optimized, through specification of more accurate time windows. The participation of 3rd party logistics introduces sufficient variance for the formulation of a robust prediction strategies.

Customer is a large fulfilment and logistics company within the technology and mobile sector.

Daily monitoring and tracking of logistic progress and key performance indicators (KPI’s) are essential services supplied by the vendor in addition to monthly reporting and data warehousing services. Mobile based reporting infrastructure via “WhatsApp”, which interface with a REST-API system produce KPI’s and metrics on demand for users with real-time data access.

  • REST-API receives request form WhatsApp interface accessible from password control user bases.
  • API extracts daily report information for 14+ reports from cloud SQL database environments and pass data via WhatsApp.

Making key strategic decisions with “on hand” information helps further optimise and streamline business process and logistic demands. 

Customer is a large fulfilment and logistics company within the technology and mobile sector.

Day to day logistic operations are mined in order to identify potential efficiency “bottle necks” and highlight concerns from delayed check points/process steps. Aggregate as well as individual operations are identified with visual integration and metrics. The overall business process is captured with the flagging of outlier or deviating process.

  • Logistic processes are compiled in full, with most common paths and deviations illustrated visually with accompanying metrics.
  • Process lag times easily suboptimal process and their respective bottle necks.
  • Suboptimal process or pathways can be extracted to individual level for follow ups or addressing more serious concerns regarding logistics.

The solution constructs overall business process pathways, identifying the most common and fastest pathways for the client. Process deviations address suboptimal processes or steps in logistic efficiency.

Customer is a local market research firm in the fast-moving consumer goods (FMCG) sector.

The Customer performs various market research activities on behalf of their clients (product and brand owners) including

  • Monitoring of in-store product placement and promotional activities.
  • Monitoring of product placements and advertisements across most national media including newspapers and magazines.
  • Monitoring and tracking product placements and tracking on major retailer websites and mobile application

Vendor is assisting the company with implementing a master data management process to improve the quality of the data analyses they can perform.

Key challenges solved includes

  • Importing the data into a consolidated data warehouse
  • Transforming dirty data into a consistent format for analysis and processing
  • Matching the inbound data against the master record using fuzzy matching and NLP techniques.
  • Presenting the data which cannot be matched with certainty to domain experts to either link to an existing record or trigger a new entry into the master data record.
  • Presenting a workflow for the review and acceptance or master data changes.
  • Categorizing and tagging the master data items into sensible groups for further analysis.

Once the product dimension has been operationalized the process will be extended to other key dimensions relating to retailers, manufacturers, shops and their attributes.

Customer is a local market research firm in the fast-moving consumer goods (FMCG) sector.

The customer is partnered with a major international energy soft drink and beverage company for which the vendor develops the internal demand and sales forecasting using AI technology. The focus on multi-variate forecasting incorporates advanced meta assembling machine learning approaches for extreme event forecasting.

The solution provides feature expansion in scenarios were limited feature are present for model construction. The use of autoencoder artificial neural networks expand demand quantities or sale values, extracting more predictive patterns. These expanded features are passed on to secondary LSTM models, which incorporate other feature values such as product information, weather and other miscellaneous features for final forecast predictions.

  • Regional sales data from major retailers collected with near daily resolution use.
  • Supplier data for multiple products on hand.
  • Historic info on past promotional activities including price discounts and other special offers.
  • Information on the promotional activities for other competing brands in the same region and distribution channel at the same time.
  • Multi-variate forecasting accounting for extreme forecasting circumstance using a two-step meta ensemble model system.
  • Integration of sales and demand data for full supply chain overview.

The results of the modelling will be presented in a planning dashboard for manager to consume and is expected to reduce stock-holding costs and provide insight into the expected outcomes of promotional and business development activities.

2. Frequently used Technologies

The following is a list of technologies typically used in customer solutions:

  • PostgreSQL
  • Microsoft SQL Server
  • MySQL
  • Snowflake
  • Exasol
  • TensorFlow / Kera
  • Other Python libraries
  • fraXses
  • Apache Spark
  • QlikView
  • QlikSense
  • Microsoft PowerBI
  • Yellowfin
  • Apache Superset