The Effect of Artificial Intelligence on the Finance Industry

Evolving technologies have always had a great impact on businesses because of how they can improve the existing process. Certain technologies offer great scope to take your business to the next level because they have the capacity to change the way you do your business. Artificial Intelligence is currently the most trending topic due to the opportunity it offers to benefit from its use. There is no other industry except the financial industry who is trying their best to adopt Artificial Intelligence for speed, accuracy and efficiency in business. Artificial Intelligence and Machine Learning offer a great deal in the finance industry through algorithms in the financial services. At the heart of the Artificial Intelligence are some of the algorithms which are self-learning and can help the finance industry if fed the right data. There are multiple fields in finance which can eventually benefit from the implementation of Artificial Intelligence and could prove to be of great value to the customer and the financial organization too.

Let’s have a look at the areas in finance which will benefit from the introduction of Artificial Intelligence;

Customized Financial Services

Artificial Intelligence has expanded the range of offerings under the finance segment based on the customer preferences for financial spending. Data accumulated by AI suggests that there should be various customizations in finance based products and services because the spending pattern of customers differs in many ways. There are some customers who look for specific offerings from a bank and he/she should receive the optimum package based on the need and want.

Reduction of cost in Finance through Artificial Intelligence

We can all agree on this because AI has definitely brought the costs down in finance by providing multiple services at an affordable price. Now-a-days the services offered by banks are comparatively low on price which is good for a customer because there are various preferences when it comes to availing a certain service. AI has made is extremely convenient for the public to make use of the financial services.

Fraud Detection

Artificial Intelligence can proactively detect if a fraud is going to take place in a financial system or not. AI makes it a point to keep all things secure and take steps towards safety before any chances of fraud. Fraud detection through AI can help bankers to follow the policies and regulations while providing a financial service to an individual. AI is expanding the financial products portfolio by continuously understanding the human psychology.

Less Human Intervention in Management

There is no longer a need for specific personnel to answer questions about financial services which are being offered and how it can help the customer. Now AI processes data to solve queries and suggest the best service or solution for an individual. Human opinions are no longer needed to forecast the demand of financial services.

Automation

Important decisions in finance cannot be inaccurate and thus AI learns and studies huge amounts of data before automating certain feature to provide a customer with accurate information. AI safeguards all the areas of automation to deliver the best results to the customer by keeping their trust.

Voice Assistance

This feature allows the user to use banking services based on voice commands rather than touching your mobile phone or any other device. Through voice based banking feature, many queries of a customer can be answered by AI with maximum ease along with transactions and other information.

Greater Insights

AI can dig deeper to get better insights into the existing data and newer data to look for trends and patterns leading to delivery of a service to a customer. With ever increasing data, AI can efficiently look into the raw data to excavate important information.

The Future

Artificial Intelligence in finance is able to continuously learn and re-learn the existing data, patterns which affect the finance industry. AI provides a great scope in developing the current products and services and also provides an opportunity to develop these existing products in the portfolio. Artificial Intelligence can regularly study the market to know what the consumers are looking for and can provide them those services before anyone in the market.

Going hand-in –hand: Big Data and Banking

Banks are digitally transforming themselves at a fast pace with advanced branchless technology and contemporary services. The latest buzzword in the fintech industry are chatbots which have been adopted by almost all leading banks to make their customer service readily available to clients round the clock. So now, what is next? Big Data? But banks across the world are already using data analytics to upscale their business. Hover, tech experts believe that banks are still to realize the full potential of Big Data. While the BFSI sector creates enormous amount of data every second, is it able to mine this voluminous amount of information?

May be it is time, say some. Big Data that is defined by volume of data, variety of data and velocity of processing the data presents big opportunities for financial institutions. Many of these have even transformed themselves with the help of data mining that eventually helps in quick, easy and apt decision making. While, banks have been slow in the adoption of this technology due to the confidential nature of its data, the trend is seeing a positive change. Let’s take a look at some advantages of deploying Big Data techniques in banking: 

1.      Risk Management

While all businesses need to engage in appropriate risk management, in banking industry this practice warrants extra attention. BigData coupled with Business Intelligence can provide vital insights to banks on risks of approving loans to potential customers post evaluation of portfolios. Big Data can also help in early detection of fraud since it locates and presents data on a single scale making it simpler to mitigate the count of risks to a controllable number.

While improving the projecting power of risk models, big data also lowers system response times and increases effectiveness. Also, along with wide risk coverage, analytics also cause vital cost savings by generating more automated processes and precise predictive systems and less failure risk. It can positively impact fraud management, credit management, loans management, operational risks, and integrated risk management.

2.      Compliance 

A heavy regulatory framework dictates the working of financial services so as to form a shield of protection against frauds and misuses. Big Data can play a crucial role in conforming adherence to regulations. It can identify and patch vulnerabilities, thereby strengthening and fortifying all materials of data governance and compliance. It can also help create baseline for ‘standard’ operations, which gives organizations a head start in detecting fraud and helps managers spot compliance and regulatory issues before they become a problem.

3.      Customer Experience

American worldwide management consulting firm McKinsey Company says that marketing productivity can be boosted by 15-20 per cent if companies use data and Big Data to make better marketing decisions. From ‘Product is King’, BFSI strategies now focus on ‘Customer is King’ and it has become important to focus on what they need and expect from a bank and financial institutions. To understand this, just a few customer snapshots won’t make the deal, a data hub needs to be created with ALL information about the customer and his interaction with the brand like personal data, transaction history, browsing history, service, and so on.

These customer insights generated by data-based analytics can empower the BFSI sector to segment customers and target them with appropriate material.

4.      Fraud Detection

Banks and financial services can and are already using Big Data analytics to distinguish between fraudulent activities and genuine business transactions. Analytics and machine learning can both help determine standard activity based on a customer's history and differentiate it from unusual behavior indicating fraud. The analysis can also suggest remedial actions such as blocking crooked transactions, deriving from actions taken in past. It will not only stops fraud before it occurs but will also improve profitability.

5.      Employee Engagement

What your employees feel about working in your company has a lot to do with what your end customers will experience. A higher level of satisfaction among employees will also extend to your customers and will push business growth. Big Data can help companies look at real-time data and not just annual reviews which are usually based on human memory. With the correct tools in place, companies can measure everything from individual performance, team work, inter-departmental interaction, and the overall company culture. When the data is related to customer metrics, it can also enable employees to spend less time on manual processes and more time on higher-level tasks.

Challenge

While there are many positives to making use of Big Data analytics in the BFSI sector, the huge amount of data that is being generated by a wide variety and number of sources poses a big challenge. A study says that, the digital universe is expected to reach 44 zettabytes (that's 44 trillion gigabytes) by 2020. Thus, imagine the amount of data that is going to be generated. Super software and computers will be needed to process such information that can halt legacy systems.

Conclusion

Once the sorting is done and useless data can be justifiable thrown out, the remaining crucial data can help banks grow from leaps to bounds. Besides, helping banks deliver better services to their customers, both internal and external, Big Data is also helping them improve on their active and passive security systems. 

Big Data is already playing a role in the banking sector with many banks and financial institutions capturing customer related data for sentiment analysis, starting from social media websites to various market research channels.

Transactional analysis is being used to fathom spending patterns of customers, assess consumer behavior based on channel usage and consumption patterns and segment consumers depending upon the aforementioned attributes, and identify potential customers for selling financial products.

Most of these findings can be applied easily into fiscal systems of banks aiding them reinforce data security and avoid any type of attack. A combination of many such transactional and sentimental gauges can help banks arrive at a holistic decision making approach and thereby implement erudite machinery, a need of the hour for the banking sector.

Big Data and Robotics

The last few months have witnessed a rise in the attention given to Artificial Intelligence (AI) and robotics. The fact is that robots have already become a part of the society; in fact, it is now an integral part. Talking about big data, it is definitely a buzz word today. The future of AI has been transformed considerably after it has been clubbed with new developments in the technological world like big data.

Enterprises worldwide generate huge amount of data. The data doesn’t have a specified format. It could be both structured and unstructured. Years back the data generated used to get wasted as there was no analytics performed on it. But now with the advent of big data, data is processed and analysis is performed on most of the data that is generated. Analysts make sure they are able to derive meaningful patterns, trends, and associations that simplify business decisions.

Big data is such a big deal these days that even small and mid-scale companies after looking at all the big data benefits wish to get benefited from it. There are ample of benefits of big data, but the biggest advantage is gathering the surprising amounts of information and then analyzing all the information that is obtained from web. The term ‘big data’ is comparatively new, but the concept has been a part of the world of robotics since a long time. The director of Auton Lab, Arthur Dubrawski says, "Robotics from the beginning has always been about data". The operational definition of Robotics is all about executing the following sequence in loop: sensing, planning and acting.

Almost all the activities going around the robot and in the surrounding environment is perceived by the robots. Robots sense and perceive through the sensors built in them in order to be aware of what’s happening around them. To meet the desired purpose and reliability in a complicated environment planning is needed. Also to meet the planned goals, taking and monitoring planned actions is a must. Did you notice all the above steps involve use of huge amount of data? There are a large number of modules meant for sensing purpose, some of them are sensors that measure range, position, visual, tactile sensor and other various similar modules. Some of these sensors generate large amount of data. Artificial Intelligence (AI) isn’t discovered in the recent years. In fact, it has been a part of the Defense Research and Development Organization (DRDO) as a Centre for Artificial Intelligence and Robotics, established much earlier in the year 1986. Robotics haven’t labelled anything, but have a long history of working with Big Data.

According to Dubrawski, “Robotic technology powered by AI has always been about analytics from its advent”. He also believes that robots have the capability of sensing and perceiving data through their sensors. They then link what they perceived along with actions through planning. Hence performing analysis and processing of information at all stages in loop of sense, plan and act. For years now we have relied on technologies and borrowed analytics from methodologies such as machine learning and many others. However, robotics come up with some original research and techniques occasionally. These techniques are usually designed for solving robotics related issues, but later they can be used for any application.

How has Big Data impacted Artificial Intelligence?

5 reasons about Big Data that promoted AI implementation:

Increased processing capability: With the evolution of processors in the recent years there has been drastic growth in computing speeds too. Billions of instructions can be processed in few micro seconds. Along with the traditional sequential computing through CPUs (Central Processing Unit), there is an advent of parallel computing through GPUs (Graphics Processing Unit) seen. This has indeed increased the speed of processing data and helped derive advanced protocols for machine learning in AI applications.

Availability of low cost and large scale memory device: High storage and retrieval of big data is now possible using efficient memory devices like DRAM’s (Dynamic Random Access Memory) and logic gate such as NAND’s. Data need not be placed at some central location or stored in a particular computer’s memory any longer now. Also there is so much of data generated and processed every day to fit into a single device. Due to Cloud technology data can be stored in distributed infrastructure and parallel processing can be done on the data. Hence, the outcome of large-scale computations like Cloud technology are used to construct the AI knowledge space.
Learning from actual data sets, no more from sample ones:

Just when AI came into existence, machines had to learn new behavior from a limited sample sets, along with a hypothesis-based approach for analysis of data. That’s a traditional way, now a day with Big Data machines don’t have to rely on samples. There is ample of actual data available that can be used anytime. Algorithms used for voice and image processing: Understanding and learning from human communication also known as Machine Learning, is a fundamental requirement of AI. Human voice data sets are a lot in number, with numerous languages and dialects. Big data analysis supports breakdown of data sets to identify words and phrases. Similar is the case with image processing, it identifies appearances, outlines, maps to process information. Big data analysis enables machines to recognize images and learn how to respond.

Open-source programming languages and platforms

If it was possible to store a data set in a single storage device, then AI data model would have used not very complex programming languages such as Python or R, which are also known for being a data analyst. Unlikely, for commercial scale operations enterprises use Hadoop for big data management. Hadoop is an open source, java based software framework that has capabilities of reading and analyzing distributed data sets. Since Hadoop is open source it is reliable and free programming tool for data analysis. It has made AI algorithm execution more efficient.

Today AI and Big Data analytics are known to be two most promising technologies that enterprises can take along with them in the days to come. AI along with Big Data will make sure businesses take intelligent decisions based on historic information available. But understanding the union and interdependency of these technologies is where the success lies.