Predictive modelling software is known for training the model with the dataset with known results to predict outcomes for the new data. The two common types of predictive models are, classification model (example, predict outcome when a component fails) and regression models (predicts a number). The benefits of predictive analysis are: • Improved production efficiency: It allows for effective inventory forecasting, production rates for meeting demand, and the like. • Improved Decision making: It identifies patterns and trends for the data, enabling easy decision making. • Enhanced risk reduction: Predictive analysis, as the name suggests, enables prediction about the future. This is most helpful for a firm to save it from the upcoming risks. • Enhanced fraud detection: Being aware of the trend, a change becomes helpful in detection of fraud. • Targeted, personalized marketing campaigns: Predictive analysis helps in knowing the structure of the market and helps in closely targeting and personalizing marketing campaigns to attract customers. Read more at: https://blogs.opentext.com/predictive-analytics/

The ways in which financial enterprises operate today have changed fundamentally due to technological evolutions. Titanic amount of data gathered from individuals’ electronic devices can be analyzed and used by the Fintech sector to deliver reliable services to its clients. Big data have been offering numerous benefits to this industry in areas of fraud detection, customer segmentation, risk management etc. Big data helps financial enterprises understand the transaction pattern of clients and can inform them about suspicious transactions. Big data also partially, if not fully, rules out the potential threats from bad investments and bad payers. Fintech companies aim at creating personalized financial services and big data assists in doing so by meeting the specific demands of the final customers. Big data also leads to better compliance capabilities ultimately translating into better services for both B2C and B2B consumers.

Read More at: https://www.smartdatacollective.com/fintech-big-data-play-role-financial-evolution/

 In choosing a payment gateway one must not only consider the logistic and technical hurdles, but also the security it provides. It is a merchant service used as third party to authorize credit transactions. First, you need to decide whether you need a classic; require you to apply for a direct merchant account or modern setup; allow you to use their services without one and are easier to set up, but have higher. Most gateways strive for compatibility though they are easier to integrate. In order to maximize the average user experience and make transactions fast, a processor that can secure credit card authorization in seconds matters. A gateway with thorough reporting features is required. If a business plans for handling huge data it has to look for fraud detection and other security features. One with built-in invoicing capabilities and better usability. Finally, you’ll need to consider the costs and fees associated with each payment gateway. There is no perfect payment gateway out there, so one need to find the best fit for your specific business. Read more at: https://www.entrepreneur.com/article/294964

Big data and financial services have played their role together to boost the company's profits. Big data is providing advantages to financial services in three ways. Firstly, big data is keeping financial companies ahead to develop more creative and innovative ways of using big data to predict future, which companies are going to earn profits and which type products will be demanded in future. Nowadays, financial companies can easily monitor and respond to changes that exist in financial markets. Secondly, banks are also using big data to become more customer focused. This can be done by tapping the reams of unstructured data like lifestyle information, social media activity, and customer feedback and support requests. Thirdly, big data can be used in fraud detection. Read more at:http://www.smartdatacollective.com/bernardmarr/335942/3-ways-big-data-changing-financial-institutions-forever

Faced with challenges of bureaucratic vices and realities, government agencies often let things slip through the cracks, including fraud. It is disheartening to learn that huge losses are incurred by many public facing agencies due to fraud and such losses are regarded as expected operating costs. Yet, no measures are being employed. Thankfully, investments are being made in predictive analytics tools by agencies and some progress has been achieved in detecting preventing and prosecuting fraud. However, to tackle crimes effectively the tools need to be comprehensive, flexible and affordable. For example, dynamic case management solutions can be applied to tackle the mammoth challenges faced by the agencies. Read more: http://gcn.com/articles/2015/06/10/fraud-control.aspx

Today,banks are using analytics to control frauds in electronic payments. Private banks such as HDFC Bank have implemented analytics software. The thing is that the out-of-the way transaction would decline if one fails to respond to a phone call or message immediately after the transaction. There are two kinds of fraud detection in payments- One is during the transaction, and the other is using analytics to identify suspicious transactions based on past behaviour. Banks are now specializing in the analytics part. According to an official of an analytics software company that provides banks with software to detect frauds, the software can be used to personalize services, like ATMs, for customers depending on their preferences. Read more at:http://timesofindia.indiatimes.com/business/india-business/Banks-use-analytics-to-check-fraud-in-online-payments/articleshow/38347410.cms

Fraudsters are more competent, better made, and creatively excellent than whatever possible time in the later past. Their adulteration arrangements include complex frameworks of individuals, records, and events. The evidence for these schemes may exist on multiple systems, incorporate various data sorts, and deliberately represent hidden activity. So an analyst has abundant investigative focuses on these frameworks with no true approach to join data or results. To prevent and uncover deception, one needs a solution that is more exceptional and advanced than hoaxers. A basic venture in fraud detection analytics is visualizing the patterns in your data between people, places, frameworks, and events. These data mining and profound analysis capabilities provide more context and better information, enabling more accurate data segmentation and data labelling, which further improves pattern recognition. To read more about it: http://www.21ct.com/solutions/fraud-detection-analytics/.

According to a recent EY (formerly Ernst & Young) survey, very few Indian companies use ‘forensic data analytics’. Seventy-two per cent of the 500-odd companies surveyed believe Big Data has the potential to mitigate frauds. However, only seven per cent are aware of specific Big Data technologies and only two per cent are using such technologies. There are 2 basic reasons behind the sceptical mindset among Indian companies about using big data to detect frauds. First is shortage of big data skilled workers & second is their reluctance to invest in these technologies.

To know more, visit the following link:

http://www.thehindubusinessline.com/features/smartbuy/why-we-arent-using-data-analytics-to-detect-fraud/article5897189.ece