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P2P lending platforms appeared on our browsers, and new papers, like crypto currencies, right after the financial crisis. P2P platforms are platforms in which two parties (Borrower and lender) directly contracts with each other. In this idea, banks are not intermediating anymore between lenders and borrowers. The P2P platforms do not lend their own funds but act as facilitators to both the borrower and the lender. Additionally, the platforms do the credit scoring and make a profit from arrangement fees, not from the spread between lending and deposit rates. This thereby cuts out traditional banking protocols. However, not all peer-to-peer platforms work the same way. For example, some platforms allow potential lenders to pick their borrowers, other oblige them to lend to all those approved for credit. This diversity of modus operandi of platforms is meant to provide specific types of services for different type of users. Experienced lenders, such as companies, would rather choose themselves who they want to lend to. Whereas, novice investors only aims at getting extra cash inflow, and therefore let platforms choose for them. All in all, the P2P platforms provide lower-interest rates, lower asymmetry of information, simplified applications, and accelerated decisions. The biggest platforms for consumer lending are Lending Club, Prosper and Sofi and have issued, by 2015, 1 million loans and are generating more than $10 billion a year. In this idea, P2P platforms seem to be very attractive in time of economic downturns by which lots of households are not able to get a mortgage or loan for reasonable interest rate. Banks have started to consider the threat P2P platforms could represent. Goldman Sachs (being one of the largest investment bank in the world) estimated that when peer-to-peer comes of age, it could reduce profits at America’s banks by $11 billion, or 7% (Fawthrop, 2019; the Economist, 2015; Investopedia, n.d). That being said, it seems so far, that peer-to-peer platforms seem to replace somehow the work of banks.

 

However can we consider this technological based service as a disruptive innovation? And will this new perspective of investment replace, the incumbent financial institutions?

 

Sources:

 

Fawthrop, A. (2019). Peer-to-peer lending platforms remove banks from the investment equation. NS Banking. Retrived on the 14^th of October 2019 from:

https://www.nsbanking.com/analysis/peer-to-peer-lending-platforms/

Investopedia. (n.d). The 6 Best Peer-to-peer Lending Websites. Investopedia. Retrieved on the 14^th of October 2019 from: https://www.investopedia.com/articles/investing/092315/7-best-peertopeer-lending-websites.asp

The Economist. (2015). From the people, for the people. The Economist. Retrieved on the 14^th of October 2019 from: https://www.economist.com/special-report/2015/05/07/from-the-people-for-the-people

 

 

 

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Medicine normally rather prevents than cure, and cure in case of diagnosed disease. Unfortunately, still nowadays, doctors and surgeons attempt to cure rather than preventing, which lead generally to diverse extensive costs (Physical, emotional, monetary, and budgetary) to the patients, to their surroundings, and to the society they live in. This is especially the case in the oncology area. In the U.S on average 439,2 per 100,000 men and women are diagnosed of cancer every year, and 163,5 per 100,000 men and women every year die from it . Roughly speaking, 1 man of 2 and 1 women out of 3 who got diagnosed by cancer will die from it. One of the main causes of this high rate of death can be due to the fact that doctors diagnose the disease way too late (National Cancer Institute, n.d).

In this regards, Artificial intelligence might be a partial if not a absolute solution. In 2016, Watson, an AI machine of IBM saved a Japanese female patient of 60 years old by diagnosing a rare leukaemia (blood cancer). Other tests realised that Watson could detect skin cancer on patients at a precision of 90%, while oncologist detects at a rate of 85% of all cases. What’s more interesting is mixing Watson and the oncologists’ opinion increase this rate to 95% of detecting capacity (Sicara, 2019).

Furthermore, a team in AI laboratory of the MIT and of the Massachusetts General Hospital (MGH) has created and trained a deep learning model capable of predicting whether a patient is highly likely of developing a breast cancer in the future, based on breast radio (mammograms). The algorithm was trained on 60 000 cases and had learnt to detect subtle patterns signals in mammary tissues to predict a future cancer ( Conner et al., 2019).

Nowadays, medical machine learning are far from being perfect, as there is a fear of « adversarial attacks », which is the manipulations of fragments of data that can alter the behaviours of AI. In other words, by wrongly coding the input, the machine learning would provide wrong output meaning in the medical area that it would diagnose a disease on patient that has none (Sicara, 2019; Finlayson et al., 2019).

 

However, due to the true potential that AI presents, do you think that, in the future, it would replace to a certain extend or completely doctors when it comes to diagnoses of disease? Would AI contribute to the prevention of potential diseases?

 

Sources:

Conner, A., Gordon, S., & Gordon, R. (2019) Using AI to predict breast cancer and personalize care. MIT CSAIL. Retrieved from: https://www.csail.mit.edu/news/using-ai-predict-breast-cancer-and-personalize-care

 

Finlayson, S. G., Bowers, J. D., Ito, J., Zittrain, J. L., Beam, A. L., & Kohane, I. S. (2019). Adversarial attacks on medical machine learning. Science, 363(6433), 1287-1289.

 

National Cancer Institute. (n.d). Cancer statistics. Retrieved from: https://www.cancer.gov/about-cancer/understanding/statistics

 

Sicara. (2019). Quand les médecins arrêteront de nous soigner. Retrieved from:

https://www.sicara.fr/parlons-data/2019-05-20-intelligence-artificielle-medecine-predictive