Fog Computing and its role in Banking Industry

Fog computing which sometimes also referred as the Edge computing is the process of the doing the computations locally and then passing over the results to Cloud processes.

The need arises when IoT devices came into picture and when cloud systems were started to be overwhelmed with processing of RAW data over cloud computing resources. This yielded a need to process the raw data in local storage and with compute power of the IoT devices and sending the processed data over the internet to save cost and efforts in terms of network usage, saving cloud computational power and cloud storage.

All IoT devices generate terabytes of raw data from sensors or from local transactions, instead sending everything to cloud, role of fog computing is to do as much processing as possible using computing units co-located within the data-generating devices, so that

1. Processed rather than raw data is forwarded.
2. Bandwidth requirements are reduced.
3. The latency between input and response is minimized.
4. Preserve the raw data where it will be used rather than bring it back to same device again when needed.

 Definition

“Fog computing is a decentralized computing infrastructure in which data, compute, storage, and applications are located somewhere between the data source and the cloud. Like edge computing, fog computing brings the advantages and power of the cloud closer to where data is created and acted upon.”

Fog computing Architecture

Though it’s not a separate system altogether it’s a layer sandwiched between cloud and physical devices.

Fog computing implementation involves either writing or porting IoT applications at the network edge for fog nodes using fog computing software, a package fog computing program, or other tools. Those nodes closest to the edge, or edge nodes, take in the data from other edge devices such as routers or modems, and then direct whatever data they take in to the optimal location for analysis.

In connecting fog and cloud computing networks, administrators will assess which data is most time-sensitive. The most critically time-sensitive data should be analyzed as close as possible to where it is generated, within verified control loops.

The system will then pass data that can wait longer to be analyzed to an aggregation node. The characteristics of fog computing simply dictate that each type of data determines which fog node is the ideal location for analysis, depending on the ultimate goals for the analysis, the type of data, and the immediate needs of the user.

Advantages and disadvantages of Fog Computing

Advantages

1. Less network traffic: Fog computing reduces traffic between IoT devices and the cloud.
2. Offline availability: In a fog computing architecture, IoT devices are also available offline.
3. Cost savings through use of third-party networks: Network providers bear high costs for high-speed upload to the cloud. Fog computing reduces these.
4. Data security: In fogging, device data is preprocessed by the local network. This enables implementation in which sensitive data can remain internal to the company or be encrypted or anonymized before being uploaded to the cloud.

Disadvantages

1. Additional network security requirements: Fog computing is vulnerable to man-in-the-middle attacks
2. Increased maintenance requirements: Decentralized data processing requires more maintenance, since controllers and storage locations are distributed across the entire network and, unlike cloud solutions, can’t be maintained or administered centrally.
3. Little protection against failure or misuse: Companies relying on fog computing must equip IoT devices and sensors with controllers that are difficult to secure against breakdown or misuse, e.g., in manufacturing facilities at the edge of the network.
4. Higher hardware costs: Fog computing requires that IoT devices and sensors be equipped with additional processing units to enable local data processing and device-to-device communication.

Fog Computing in Banking Industry

Fog computing is the means of distributed processing in payment domain, recommending personalized recommendations and offers and attracting new generation of customers through revolutionary payment methods like apple pay, Samsung pay any other on device financial transactions not limited to payments but can be extended to risk assessment and trading platforms.

There are several use cases where fog computing became an integral part of the functionality implementation for various financial institutes across the globe. Some examples but not limited to:  

1. Citibank employs beacon technology to enable consumers to access ATMs using their smartphones.
2. Small financial institutes building the analytics and analysis algorithms to run on the local devices rather than building expensive cloud-based solutions
3. Provide insurance providers an insight of the real time driving habits of drivers and vehicle conditions.

Today’s highly competitive banking, driven in part by the rapid growth of new computing paradigms, together with Financial Technology (Fintech) is pushing the industry to look for ways to continue improving customer relationships. Analytical processes in Cloud environments can leverage large volumes of data to perform computational processing including machine learning techniques to improve reliability, automated configuration, and performance

In the field of e-business, one way of achieving this is through personalized product recommendations. Banks participate in con- tent customization methods to expand and align them- selves with new digital business mechanisms. In digital businesses, recommendation systems provide users with intelligent product search mechanisms that are adapted to their preferences. The in- crease in the sale of this type of systems is a consequence of their ability to interact with users to help them choose and discover products and services that are of interest to them. In this sense, the recommendation systems are designed to adapt to each user, becoming a kind of personalized assistant that facilitates access to the many products offers in a more efficient way.

Fog computing can be used to showcase predictions in areas of financial products, such as mortgages, loans, retirement plans etc.

Fog Computing Architecture Diagram