Axisoft

Axisoft Axisoft is a top-notch financial technology provider with offices in China (Hong Kong, Shanghai, Zhuhai, and Shenzhen) and Singapore.

03/08/2020

AlchemyJ allows you to work with complex calculations in Excel.

It also easily works with sources like External APIs, Databases, Files, and API Parameters on both input and output. Then AJ can turn it into a component. This component can easily plug-in into your application and work flawlessly together.

In the end, the system input will be sent to your application. The calculation component embedded will provide you with the final results.

Go to http://alchemyj.io, and download our community edition to try it out today.

30/07/2020

Riding the wave of API, banks start to implement MicroServices as an API framework. Some banks even see it as an antidote to many problems.

Such as performance, resilience, scalability, etc. Some people even claimed the future of core banking is also on MicroServices.

MicroServices has the undoubted capability in solving many problems but it has its limitations. Here we list 3 major challenges of implementing MicroServices in Banks.

1. Transaction Management
2. Service Performance
3. Application Partitioning

Transaction Management:

Transaction Integrity in Distributed Computing is always a problem.

Since Microservices is the technology of distributed computing, when transactions are taken place in different microservices or different servers, the overall integrity of these transactions are difficult to be managed.

For example, you can deduce money from one account but fail to deposit money to another account.

There are several solutions for this problem, such as Distributed Transaction Coordinators, its slow in performance.

The other solution is to adopt the event subscription architecture. It may have a window of data inconsistency.

Our suggestion is to avoid the distributed transaction. So you partition your microservices in the way that all transactions are taken in one service.

Service Performance:

Although MicroServices is able to distribute the load to different servers, it does not guarantee the performance of each individual call.

This is because each individual call may trigger a number of microservices, all microservices communicate over a network, and the efficiency, latency, reliability of the network never guarantees.

Application Partitioning:

The architecture approach of traditional applications is Monolithic. Software architects need to adopt a new architecture approach of microservices.

They have to break down the single application into many small services, each service can run and be managed independently.

The application partitioning is a case by case exercise each application.

In addition to technical knowledge, designers also need relevant domain knowledge.

We consider the domain knowledge, is even more important than technical knowledge.

For instance, if you are going to develop a microservices-based OMS. you need to know the Life Cycle of Securities Trading. So that you can define the system boundary well.

We see there is a hype in the banking sector for the use of microservices. But it is not a silver bullet. There are cases completely not suitable for microservices.

We have seen a case of system migration from a database-centric application. It has over 100 thousands line of code in stored procedures, to microservices architecture. Needless to say, the project ended up in failure.

In the nutshell, the monolithic approach has many advantages.

Whether the microservices approach fits you, depends on the problem you are dealing with.

Thank you for watching this video. Please subscribe to our youtube channel, so you can receive regular update of the financial technology. You are also very much welcome to visit our official web site. Thank You.

30/07/2020

As Artificial Intelligence is getting mature, banks have been exploring the use of AI. However, there are 3 challenges they need to know

The first one is Predictability. Nowadays, AI is driven by Neural network, which is the most popular machine learning technology. The neural network learns through a huge amount of training data.

Different data will lead to a different result. As long as training data is fed to the neural network continuously, the neural network will evolve itself and produce an unpredictable result.

The second one is Explainability. You cannot explain why you have the result from the neural network. No matter it is a correct or incorrect result.

The neural network is just a black box, with tens of thousands of values. They are responsible for producing your result. But these values make absolutely no sense, to the human being. So you cannot explain, why the result was produced.

The third one is Training Data. As the knowledge of the neural network is built through training. The training process requires a huge amount of processed data.

In addition to the quantity, data quality also determines, the capability of the neural network. Lack of the number of training data, or poor data quality, will lead to the neural network inaccuracy. So data quantity and quality, are the most important factors in the neural network training.

These challenges are not going away in the coming few years, they will be the hurdles, of making use of AI seriously in banks. Currently, AI in banking sectors will only be used in supporting roles in business operations.

In Axisoft, we have been building a neural network to learn the meaning of financial terms. This is the foundation of many AI applications in banks, such as chatbot. To get regular update of financial technology, please subscribe to our youtube channel. Thank you.

3 Challenges in the Artificial Intelligence (AI) Adoption for Banks

20/07/2020

What is Banking API?

For laymen, Banking API brings user request, such as "What is my account balance?", to the bank’s server. Then the programs on the server work out the response of the request, such as "Your account balance is 1000". Then it sends the request back to the users.

In technical terms, its a message transmission and processing system.

We can classify banking API into 4 categories:
1. Non-sensitive information Enquiry, such as product information
2. Sensitive information Enquiry, such as account balance
3. Non-Realtime based Transaction, such as loan application
4. Realtime based Transaction, such as payment

Banking API is not something new, but extensive adoption of Banking API will change the landscape of the banking industry. Whoever seizes this opportunity are the winners in the future.

In fact, the major implementations of the Banking API are done by FinTech companies. When users use the Fintech applications, they will use the Banking API indirectly. Hence banks could have new channels to provide service to potential clients.

Currently, there are two approaches for Banking API Implementation. They are
1. API Embedding
2. API Collaboration

The typical example of "API Embedding" is product comparison, such as loan product comparison. The FinTech application will call the API from different banks to request loan information, then consolidates the information, and passes the comparison to end-users. That may help them make decisions efficiently.

For API Collaboration, Fintech will build their own API based on Banking API, and other Fintech, or even banks will call their API. That forms an API network, hundreds and thousands of API are collaborating.

Currently, regulators in global financial centres, such as Singapore and Hong Kong, are advocating the Banking API.

In Singapore, the Monetary Authority of Singapore has formed an API Exchange. It is a global Banking API marketplace that links up Fintech with Banks. In 2020, Axisoft was invited to joined APIX, and provide her Banking API to banks in the global market.

In Hong Kong, HKMA has asked banks to launch Banking API in four phases. At the time of this video was prepared, banks had been planning the phases of Account Information and Transaction.

For more information on Banking API, please contact Axisoft. Thank You.

20/07/2020

We have been providing financial technology to the banks in Asia for over 20 years. Today we have over 150 members in Hong Kong, Singapore and China.

Our clientele includes financial institutions from all over the world, such as Hong Kong, Singapore, Switzerland, Australia, Japan, China, Taiwan and Macau.

In the last 20 years, we have been receiving various awards from the industry. This is another recognition of the quality of our solutions and service.

Our service and solutions cover key operations of banks. We help banks to implement, enhance and maintain their core banking system.

In addition, we also provide 7x24 run-the-bank service from our operation center. So banks could have a more reliable service to their clients.

Apart from our service, we provide our clients with a banking solution - COMPASS. It is a banking API framework, and contains all the key business operations, that crystalize our 20 years experience in the banking sector.

Besides, our OpenAPI building tool, Alchemy J, makes the development of banking API efficient and simple without compromise the stringent requirements of banking solutions.

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